Why You'll Love This Role

You will work closely with the founding team and a group of highly-skilled engineers to shape the future of search and analytics of observability data. You will play a central role in bringing integrating cutting-edge Generative AI technologies with the Cribl Product suite to help solve real customer problems.  You will work closely with development partners and key stakeholders to iteratively design, develop, and deliver products and surfaces that will delight our customers.
On top of it all you will have fun. Cribl strives to be a great place to work for everyone. 

As An Active Member Of Our Team, You Will...

• Work with the product engineering teams to prototype and experiment with new AI features
• Work across backend, frontend, and interact with LLMs and/or other ML models
• Productionize, launch, and operate AI-based technology integrations into Cribl’s core products with the goal of solving real customer problems
• Stay up-to-date with the latest AI technologies and trends
• Work closely with fine tuning models, Prompt Engineering, and Inference Platforms
• This position will require stand-by, on-call, or off-hours duties

If You've Got It - We Want It

• Bachelor's degree in CS/EE with 6+ years of industry experience
• Ability to problem solve from first principles and maintain focus on overarching objectives
• Prior experience in contributing to products that have gained significant user traction or scaled to a large user base
• Experience as an AI/ML Engineer
• Experience with ML Ops
• Experience with Typescript/JavaScript and either Node or React

Salary Range ($200,000 - $250,000)

The salary for this role is dependent on geographic location. The salary offered within the range described will be based on the individual candidate’s job-related knowledge, skills, and experience.  In addition to a competitive salary, Cribl also offers a generous benefits package which includes health, dental, vision, short-term disability, and life insurance, paid holidays and paid time off, a fertility treatment benefit, 401(k), equity, and eligibility for a discretionary company-wide bonus.

Most engineers ship features. You'll ship outcomes, directly in the hands of customers, on live systems, where it actually counts. And you'll do it faster than anyone thought possible.

The Company

Sendbird is on a mission to build the AI workforce of tomorrow. For over a decade, we built the infrastructure behind conversations—chat, voice, video, messaging APIs—and became the #1 CPaaS platform for in-app communications. 4,000+ brands trust us. 7 billion messages flow through our platform every month. 300 million monthly active users.

We powered conversations for DoorDash, Match Group, Noom, Yahoo Sports, Rakuten, and thousands of others. We were good at what we did. Really good.

We also saw it early: AI would fundamentally reshape how businesses talk to customers. The infrastructure we'd spent a decade building would become commoditized. The value would move up the stack—into intelligence, into experience, into outcomes.

We had a choice: protect what we built, or reinvent ourselves.

We chose reinvention.

In December 2024, we made the full strategic pivot to AI-first customer experience. By February 2025, we'd launched our AI agent for enterprise CX—built on a decade of conversation data, now with intelligence on top. And in November 2025, we rebranded to Delight.ai.

The name says it all. AI's real promise isn't efficiency or cost savings. It's giving customers back something they lost—the feeling of being truly understood and cared for. Not satisfied. Delighted.

The Product

Delight.ai is the AI concierge for customer experience. Most AI agents forget you the moment the conversation ends. Ours doesn't. Delight.ai builds memory over time, learns preferences, and connects context across every channel—chat, SMS, email, voice, WhatsApp—without losing the thread. We're building AI that makes customers feel understood, seen, and remembered.

Why Software Engineers for our AI Agents?

AI agents don't fail in demos. They fail in production, when the edge case hits, the prompt breaks, or the integration misbehaves in front of a real customer. That gap between "it works in the sandbox" and "it works in the wild" is exactly where this role lives.

We're deploying AI agents for some of the world's biggest brands. Each one has unique workflows, data, and stakes. We need engineers who can sit across from a customer, understand the problem, and build the solution fast, not hand off a spec and wait. If you treat AI as a core part of your toolkit and not an afterthought, you'll move at the speed this role demands.

The Role

You'll own the full technical lifecycle of customer AI agent deployments, from first integration to live monitoring, working at the intersection of product, engineering, and customer outcomes. This role is for engineers who use AI to compress timelines, ship higher-quality work, and want their impact felt immediately, not six months from now.

You might be this person if:

• You think in systems — inputs, outputs, failure modes — and you naturally ask "what happens when this breaks?" before anyone else does
• You've debugged something live, under pressure, with a customer watching, and you didn't flinch
• You use AI tools daily to prototype, debug, and iterate, and you've figured out how to get 10x the output without cutting corners on quality
• You read a system prompt the way a compiler reads code, spotting logic gaps, unintended behaviors, and edge cases others miss
• You've built something end-to-end, frontend to backend to deployment, and you're proud of how it actually held up
• You learn fastest by building, not by reading docs, and you've shipped something with a new API within days of touching it for the first time
• You care about what the customer actually experiences, not just whether the tests pass
• Ambiguity doesn't slow you down. You'd rather build a rough solution and iterate than wait for a perfect brief

You need to have:

• 2-3 years of hands-on software engineering experience in full-stack or backend development
• Strong Python proficiency and the ability to write clean, maintainable code under real-world constraints
• Practical experience with React and modern JavaScript/TypeScript on the frontend, plus backend frameworks like Node.js, Django, or Express
• Hands-on experience with LLM APIs (e.g., OpenAI, Claude) or agent frameworks such as LangChain
• Familiarity with RAG pipelines, knowledge base integration, and cloud deployment platforms (e.g., AWS Lambda, Vercel, GCP Cloud Functions)

What you'll actually do:

• Deploy production-grade AI agents tailored to each customer's environment, owning setup, configuration, and go-live
• Tune system prompts and agent behaviors against real-world use cases, using AI-assisted workflows to move from hypothesis to working solution faster than a traditional dev cycle allows
• Prototype and ship fast, leveraging agentic coding tools to build agent interfaces, dashboards, and backend integrations with Next.js, React, and modern APIs
• Integrate RAG pipelines and knowledge bases that give agents the context they need to perform
• Monitor live deployments with logging, metrics, and observability so issues surface before customers notice them
• Collaborate with Forward Deployed PMs, core product, and design to turn customer needs into working solutions, often building a prototype before the meeting ends
• Lead technical demos, document implementation decisions, and maintain architecture diagrams that actually reflect reality

Added Value

• Prior experience tuning or debugging AI agent performance in live, production environments - or in a forward deployed setting
• Background in 0-to-1 builds, customer-facing implementation, or technical consulting, with a track record of navigating the messiness of real deployments

What we offer:

We take care of the people who build the future. Here's some of what you can expect when you join Sendbird.

• Medical, dental, and vision coverage, on us. Sendbird covers 100% of the premium for our employees and roughly 80% for their dependents.
• Generous time off. 20 days PTO, 13 company holidays, 7 sick days, 2 rest & rejuvenation days, 1 volunteer day, and your birthday off.
• $3,500 annual "Be Your Best Self" boost. Spend it on anything that helps you grow. Gym memberships, therapy, books, courses, massages, and more.
• Parental leave for all new parents. Time to be present for the moments that matter most.
• 401(k) from day one. Auto-enrollment, flexible contributions, and access to Vanguard's investment platform.
• HSA & FSA options. Pre-tax accounts to cover healthcare, dependent care, and commuter expenses.
• Life & disability coverage, fully paid. Life insurance and AD&D at 2x your salary, plus short- and long-term disability at no cost to you.

Pay Transparency

For cash compensation, we set standard ranges for all roles based on function, level, and geographic location.  To determine our ranges, we utilize a variety of compensation data benchmarked against similar-stage growth companies.   A reasonable estimate of the current salary range for this role is $150,000 - $180,000 .  This range is specific to the San Francisco Bay market.  We consider several factors when making final compensation decisions including, but not limited to, skill sets, experience and training, licensure and certifications, and other business and organizational needs which may cause your specific offer to vary from the amount listed above.

Flexible Work Policy

We offer a flexible work schedule at Sendbird. We also value collaboration and relationship building. With those values in mind, we require all employees to gather with their team in the office three days per week as a minimum. Some of our roles require a more frequent in-office schedule. Please work with your manager to understand the office time requirements for your position.

What diversity and inclusion mean to us

There is no such thing as a perfect candidate and the best employees come from a wide range of backgrounds, experiences, and skill sets. Sendbird is a place where everyone can learn and grow. We respect, promote, and encourage diversity for equal employment opportunities and encourage you to apply if this role excites you.

Why Sendbird

We're not iterating on an existing product. We're building a new category, AI that genuinely delights customers, on top of infrastructure that's already at enterprise scale. This role puts you at the center of that: real customers, real deployments, real impact. If you want to own outcomes and help define what forward-deployed AI engineering looks like, this is the place.

Sendbird is building the AI workforce of tomorrow, and we hire that way too. Learn more about our interview process, how we evaluate candidates, and the role AI plays along the way.

Sendbird is on a mission to build the AI workforce of tomorrow. For over a decade, we built the infrastructure behind conversations—chat, voice, video, messaging APIs—and became the #1 CPaaS platform for in-app communications. 4,000+ brands trust us. 7 billion messages flow through our platform every month. 300 million monthly active users.

We powered conversations for DoorDash, Match Group, Noom, Yahoo Sports, Rakuten, and thousands of others. We were good at what we did. Really good.

We also saw it early: AI would fundamentally reshape how businesses talk to customers. The infrastructure we'd spent a decade building would become commoditized. The value would move up the stack—into intelligence, into experience, into outcomes.

We had a choice: protect what we built, or reinvent ourselves.

We chose reinvention.

In December 2024, we made the full strategic pivot to AI-first customer experience. By February 2025, we'd launched our AI agent for enterprise CX—built on a decade of conversation data, now with intelligence on top. And in November 2025, we rebranded to Delight.ai.

The name says it all. AI's real promise isn't efficiency or cost savings. It's giving customers back something they lost—the feeling of being truly understood and cared for. Not satisfied. Delighted.

Delight.ai is the AI concierge for customer experience. Most AI agents forget you the moment the conversation ends. Ours doesn't. Delight.ai builds memory over time, learns preferences, and connects context across every channel—chat, SMS, email, voice, WhatsApp—without losing the thread. We're building AI that makes customers feel understood, seen, and remembered.

About the Role

Most marketing teams are bolting AI onto a content process built for 2018. We're building one for 2026. 

The first is to produce excellent content and drive organic growth. You're an SEO expert across keyword research, on-page, technical, content clusters, and measurement, and you're hungry to figure out GEO, because nobody's a GEO expert yet and the playbook is being written in real time. Own the strategy and the execution that make Delight.ai discoverable wherever buyers and LLMs research: Google, Perplexity, ChatGPT, Claude, Reddit, the podcast feeds CX leaders actually listen to. SEO and GEO aren't checklists you run after the writing is done. They shape what gets written and how. The output is long-form posts, narratives, technical explainers, podcast prep, the occasional Reddit thread that earns its own citation. The bar is high. Your work has to hold up in front of CX leaders running real programs at airlines, retailers, and B2B SaaS companies, and in front of our own product and engineering teams. Sloppy generalities won't survive either audience, and they won't earn citations either.

The second is to build the engine that lets a small team produce work like that at volume. Pipelines, agents, prompt libraries, retrieval layers, schemas, design systems. The connective tissue that turns one writer into a content function. Delight.ai is an AI concierge platform, and our marketing should look like it was made by people who actually believe in the technology they sell. We have the tokens. Use them.

You will join a small, high-agency team at the intersection of product, marketing, and web. We publish for a world where buyers and LLMs decide who gets considered before a sales conversation ever happens — and we're building Delight.ai for that same world. This role reports to the Head of Product Marketing.

If you've built systems like this before, or you're excited to, you'll have a lot of room to shape the approach.

When applying, please submit one content sample (blog post or other long-form piece) that you feel expresses your ability to excel at this role. 

How We Work

Content is an engineering discipline here. We hold quality bars, build reusable components, and design systems that let us iterate fast. The best people on this team have mastered the craft by hand. They can write a great post from a blank page, and they use AI to multiply what one person can ship.

You'll work in a stack that may include Claude, Claude Code, and other LLM-native tools for research, and building the agents that orchestrate our content workflows end-to-end. Some of the best workflows on our team started as someone's side project.

If you've built systems like this before, or you're hungry to, you'll have real room to shape the approach.

What You Will Do

• Ship excellent content. Posts, narratives, and explainers about Delight.ai's AI concierge platform, our memory and personalization, omnichannel, and Trust OS pillars, and the CX workflows they unlock. Long-form is the core, but the engine reaches into podcast prep and pitching, Reddit and community participation, and wherever else buyers and LLMs go to form opinions.
• Build the content engine that helps the team move faster and stay sharper:
• Create repeatable, AI-augmented pipelines for drafting, expert review, publishing, and updates
• Build agents and workflows that support the content development process end-to-end
• Design the AEO/GEO foundation — schema, structured outlines, citation-ready formats — that makes Delight.ai discoverable across Google, Perplexity, ChatGPT, Claude, and whatever comes next
• Be customer zero. Use our agents, our flows, our dashboards. Find what's confusing. Push on what doesn't hold up. Your content will read like you've actually deployed AI concierge agents because you have.
• Stay close to the market. Our product, the CX industry, and the way buyers and the models that serve them evaluate vendors are all moving fast. Keep up.
• Close the loop. Track what's working from content to traffic to pipeline across every surface, including which podcast appearances and community conversations actually move pipeline. Learn what humans and models pick up. Iterate.

About You

• 5+ years writing long-form for a living in content marketing, technical writing, developer advocacy, or something close. Show us work that moved something: pipeline, perception, search rankings, or all three.
• Deep SEO expertise. Years of doing the work: keyword research, technical SEO, on-page, content strategy, measurement. You can spec a content cluster, audit a site, and explain why a page isn't ranking without opening a single tool.
• Curious and opinionated about GEO. Nobody's an expert yet. You've been paying attention, you have early hypotheses about what works, and you want to be one of the people who figures it out. Not someone who waits for a best-practices guide to drop.
• Hands-on with AI tools, agents, or automation. You've deployed things, not just written about them, and you can talk through the tradeoffs you made.
• You can collaborate deeply with product and engineering, ask sharp questions, and earn trust through accuracy
• You learn quickly and can turn new domains (a new vertical, a new product capability, a new channel) into clear, comprehensive explanations
• You thrive in a fast-paced environment with high agency and strong ownership
• You have strong opinions about which AI tools work best for what, and a library of custom prompts to back them up

What we offer:

We take care of the people who build the future. Here's some of what you can expect when you join Sendbird:

• Medical, dental, and vision coverage, on us. Sendbird covers 100% of the premium for our employees and roughly 80% for their dependents.
• Generous time off. 20 days PTO, 13 company holidays, 7 sick days, 2 rest & rejuvenation days, 1 volunteer day, and your birthday off.
• $3,500 annual "Be Your Best Self" boost. Spend it on anything that helps you grow. Gym memberships, therapy, books, courses, massages, and more.
• Parental leave for all new parents. Time to be present for the moments that matter most.
• 401(k) from day one. Auto-enrollment, flexible contributions, and access to Vanguard's investment platform.
• HSA & FSA options. Pre-tax accounts to cover healthcare, dependent care, and commuter expenses.
• Life & disability coverage, fully paid. Life insurance and AD&D at 2x your salary, plus short- and long-term disability at no cost to you.

Pay Transparency

For cash compensation, we set standard ranges for all roles based on function, level, and geographic location. To determine our ranges, we utilize a variety of compensation data benchmarked against similar-stage growth companies. A reasonable estimate of the current salary range for this role is $145,000.00 – $170,000.00. This range is specific to the San Francisco Bay market. We consider several factors when making final compensation decisions including, but not limited to, skill sets, experience and training, licensure and certifications, and other business and organizational needs which may cause your specific offer to vary from the amount listed above.

Flexible Work Policy

We offer a flexible work schedule at Sendbird. We also value collaboration and relationship building. With those values in mind, we require all employees to gather with their team in the office three days per week as a minimum. Some of our roles require a more frequent in-office schedule. Please work with your manager to understand the office time requirements for your position.

What diversity and inclusion mean to us

There is no such thing as a perfect candidate and the best employees come from a wide range of backgrounds, experiences, and skill sets. Sendbird is a place where everyone can learn and grow. We respect, promote, and encourage diversity for equal employment opportunities and encourage you to apply if this role excites you.

Why Sendbird

We're not in maintenance mode. We're rebuilding how businesses connect with their customers using AI, and we're moving fast. If you want to own something real, build things that matter, and work at a company that has the infrastructure, the data, and the conviction to lead this shift, this is the place.

Sendbird is building the AI workforce of tomorrow, and we hire that way too. Learn more about our interview process, how we evaluate candidates, and the role AI plays along the way.

Role

We are looking for a Sr. Staff Software Engineer to join our Zscaler Digital Experience (Core Intelligence and Data) team. This is a hybrid role based in San Jose, CA 3 days a week, reporting to the Sr. Manager, Software Engineering. You will join the team responsible for building the world’s largest cloud security platform, helping us enhance services and increase our global footprint. You will play a pivotal role in enabling organizations worldwide to harness speed and agility through a cloud-first strategy, leveraging our multitenant architecture that serves over 15 million users.

What you’ll do (Role Expectations)

• Own agentic trouble shooting framework, framing high-impact use cases, designing workflows and playbooks, and building processes for all products
• Evaluate and integrate state-of-the-art GenAI advances to deliver reliable and cost-efficient production features, utilizing LLMs, various machine learning models, data processing, fine-tuning, and inference optimization
• Work with the world class cloud platform and data lakes for feature exploration and generation
• Handle volume data with the real time pipeline for data processing and aggregation
• Design, implement, and operate scalable production systems, specifically focusing on microservices, data pipelines, orchestration, and caching

Who You Are (Success Profile)

• You thrive in ambiguity. You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful.
• You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. True ownership involves leveraging dynamic range: the ability to navigate seamlessly between high-level strategy and hands-on execution.
• You are a problem-solver. You love running towards the challenges because you are laser-focused on finding the solution, knowing that solving the hard problems delivers the biggest impact.
• You are a high-trust collaborator. You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback—knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust.
• You are a learner. You have a true growth mindset and are obsessed with your own development, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose.

What We’re Looking for (Minimum Qualifications)

• BS in Computer Science with 8+ years of experience, or MS/PhD with 5+ years of experience solving real-world problems using AI/ML and distributed systems
• Proficiency in programming, data structures, algorithms, and machine learning, with exceptional problem-solving skills driven by first-principles thinking
• Hand-on experience with AI modeling, including feature generation, prompt engineering, evaluations and productionization
• Experience in the full lifecycle of ML models, including building, deployment, monitoring, and optimization
• Expertise in designing and operating distributed microservices using tools like Kubernetes and Docker, and writing production-grade code in Python, Go, or Java

What Will Make You Stand Out (Preferred Qualifications)

• Experience fine-tuning and deploying proprietary SLMs/LLMs at scale, with a focus on optimizing latency, cost, safety, and evaluations
• Experience delivering production-ready AI systems, including expertise in abnormally detection, event correlation and incident investigation
• Proven ability to design and implement high-performance, resilient systems with well-defined service-level objectives

#LI-Hybrid

#LI-BH1

Role Description

As a Senior Value Engineer specializing in the Public Sector, you are pushing the envelope in solving critical operational challenges for State and Local Government agencies. You will be working with our most strategic public sector clients, understanding their unique objectives—from enhancing citizen services and modernizing legacy systems to optimizing grant management and regulatory compliance—and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with top AI and ML technology partners, such as Microsoft, OpenAI, and Databricks.

With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands the complex realities of government operations (breaking down agency silos) and enables agencies to industrialize AI. This unlocks real ROI on AI deployments, maximizing taxpayer value at scale. There is no AI without PI. You will prototype these solutions, demonstrate their value to government CIOs and Agency Directors, and ensure successful implementation, adoption, and value realization to increase the footprint of Celonis across state and local governments.

Key Responsibilities:

• AI Discovery & Solutioning: Understand public sector AI strategies and specific agency challenges (e.g., benefits administration backlogs, procurement bottlenecks, public safety resource allocation, or permitting delays). As a Celonis product and government domain expert, find the best problem-solution fit and translate agency requirements into innovative solutions that deliver measurable public impact.

• Pre- and Post-Sales Execution: Actively drive the full customer lifecycle within the public sector. Lead technical discovery and capability demonstrations during the complex government pre-sales and procurement (RFP) cycles, and remain deeply involved post-sale to guide implementation, ensuring agreed value and adoption thresholds are successfully met.

• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude, and don’t shy away from complex legacy processes. Leverage cutting-edge AI technologies to rapidly build creative prototypes in agency hackathons, solving critical pain points to improve constituent experiences.

• Agentic Process Transformation: Support our government customers in achieving real value out of AI deployments at scale, enabling a fundamental shift from traditional, rigid, paper-heavy workflows to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform (e.g., intelligent case triaging or automated compliance checks).

• Proof Projects: End-to-end execution of critical Proof-of-Value projects. This includes architecting and delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails, while seamlessly integrating with government enterprise data, identity protocols, and stringent compliance/security frameworks (e.g., StateRAMP, HIPAA, CJIS).

• Domain & Industry Leadership: Serve as the internal and external technical subject matter expert for the State & Local Government vertical, scaling knowledge across the organization regarding agency processes and public sector nuances.

Requirements:

• 5+ years of experience leading technical pre-sales and post-sales engagements specifically within the Public Sector (State & Local Government). This includes navigating government procurement cycles, building compelling ROI business cases for public funds, and guiding technical implementations through to constituent value realization.

• Deep understanding of business processes native to state and local governments (such as Health & Human Services, Procurement, Finance, DMV operations, or Public Safety) with the ability to translate high-level policy or agency needs into specific, impactful AI use cases.

• Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., intelligent constituent-facing chatbots, automated processing of policy documents, or grant application analysis).

• Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.

• Strong presentation skills to both internal and external stakeholders (including elected officials, agency directors, and IT leadership), whether leading technical whiteboarding sessions or formal readouts and demos.

• Bachelor’s Degree required; Master's Degree in computer science, engineering, mathematics, or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails suited for highly scrutinized public sector environments.

• Experience in deploying and monitoring models at scale within government-compliant cloud environments (e.g., AWS GovCloud, Azure Government).

• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.

Visa sponsorship is not offered for this role.
Learn More about Value Engineering at Celonis Here

Role Description

• As an Applied AI Engineer you are pushing the envelope in solving business-critical problems for our customers. You will be working with our most strategic customers, understanding their objectives and key challenges, and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with the largest AI and ML technology partners, such as Microsoft, OpenAI and Databricks. With Celonis’ Process Intelligence (PI) platform we feed operational context to AI so it understands our customers’ business and enables them to industrialize AI unlocking real ROI on AI deployments and at scale. There is no AI without PI. You will prototype these solutions, demonstrate their value to Executives and ensure successful implementation, adoption and value realization in order to increase the footprint of Celonis at those customers.

Key Responsibilities:

• AI Discovery & Solutioning: Understand customers AI strategy and business critical challenges. As Celonis product & domain expert, find the best problem-solution fit and translate customer requirements into innovative solutions that move the needle
• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude and don’t shy away from complex problems. Leverage cutting edge AI technologies to rapidly build creative prototypes in customer hackathons solving business critical problems
• Agentic Process Transformation: Support our customers in achieving real ROI out of AI deployments at scale enabling a fundamental shift in business operations from traditional, rule-based automation to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform
• Proof projects: End-to-end execution of business-critical Proof-of-Value projects, incl. architecture and deliver secure, scalable LLM/agent systems with RAG, tools, and guardrails; integrating with enterprise data, identity, and compliance frameworks.
• Ensure Successful Project Outcome: Applied AI Engineers stay involved with projects until agreed value & adoption thresholds are reached
• Specialize in Domains or Industries: To scale knowledge across the organization Applied AI Engineers specialize in domains (e.g. supply chain) and industries

Requirements:

• 7+ years of experience leading technical pre-sales, including defining AI roadmaps, building compelling ROI/TCO business cases and prototyping of machine learning and generative AI solutions.
• Understanding of generative AI techniques like RAG, few shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning that are used to build high-impact use cases like intelligent chatbots and automated text processors.
• Understanding of business processes across sectors (such as Supply Chain or Finance) with the ability to translate high-level business needs into specific AI use cases.
• Good knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.
• Strong presentation skills to both internal and external stakeholders (including executives), whether whiteboarding sessions or formal readouts and demos.
• Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails.
• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
• Experience in deploying and monitoring models at scale across major cloud platforms (AWS Bedrock, Azure AI, GCP Vertex)

Join the IT Engineering team as a Staff AI Enablement Engineer, where you will serve as the primary driver of AI enablement and solutions engineering across all departments at Checkr. This is a highly visible, hands-on role that blends deep technical expertise with a passion for teaching and empowering others. You will lead the charge on understanding team-specific requirements, configuring and evaluating AI tools, building bespoke AI-powered solutions, and standing up templatizable workflows that scale across the organization. From running office hours and hackathons to creating reusable resources and training materials, you will be the go-to person for helping every team at Checkr unlock the full potential of AI.

What you’ll do:  

• Requirements Gathering & Enablement: Embed with internal teams across departments to deeply understand their workflows, gather requirements, identify high-impact AI use cases, and translate business needs into actionable technical solutions.
• Lead Office Hours & Hackathons: Own and facilitate recurring AI office hours for drop-in support and guidance. Design and lead company-wide AI hackathons that inspire experimentation, knowledge sharing, and the discovery of innovative use cases.
• AI Tool Configuration & Evaluation: Evaluate, configure, and recommend the right AI tools and platforms for the organization. Stay on top of the rapidly evolving AI tooling landscape and make informed build-vs-buy recommendations.
• Build Bespoke AI Solutions: Design, develop, and deploy custom AI solutions tailored to specific team needs—leveraging LLMs, RAG architectures, agentic workflows, and automation frameworks to solve real business problems.
• Create Resources & Artifacts: Develop reusable playbooks, prompt libraries, workflow templates, how-to guides, and reference architectures that enable teams to self-serve on AI adoption.
• Templatize AI Workflows: Architect and implement standardized, templatizable AI workflows that can be adapted and deployed across multiple teams and use cases, driving consistency and scale.
• Training & Teaching: Lead hands-on training sessions, workshops, and demos to teach teams how to effectively use AI tools and integrate them into their day-to-day work. Act as a force multiplier by building AI fluency organization-wide.
• Cross-Functional Collaboration: Partner closely with Engineers, Business Systems, Security, Data Engineering, and other AI Solutions Engineers to integrate AI technologies into existing systems, ensuring security, compliance, and scalability.
• Strategic Planning & Roadmapping: Contribute to the AI strategy and roadmap at the organizational level, aligning initiatives with business priorities and ensuring AI investments deliver maximum value.
• Communicate & Evangelize: Present findings, demo solutions, share progress updates, and advocate for AI adoption with stakeholders ranging from individual contributors to executive leadership.

What you’ll bring:  

• Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience. A graduate degree is a plus.
• 5-7+ years of experience in engineering, solutions engineering, or technical enablement roles, with a track record of deploying solutions that measurably enhance productivity.
• 4+ years of hands-on experience developing AI-centric products and solutions, including machine learning, generative AI, LLMs, RAG architectures, agentic frameworks, and prompt engineering.
• Deep proficiency in Python and experience with AI frameworks and tools such as LangChain, LangGraph, LlamaIndex, OpenAI API, and similar; familiarity with containerization (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure).
• Demonstrated ability to lead enablement programs—including running office hours, hackathons, training sessions, and creating scalable documentation and resources.
• Proven experience gathering requirements from non-technical stakeholders and translating them into well-scoped technical solutions.
• Exceptional ability to tackle open-ended, ambiguous problems in unstructured environments, synthesizing complex information into actionable plans and deliverables.
• Strong communication and presentation skills, with the ability to make complex AI concepts accessible to diverse audiences—from engineers to executives.
• A builder’s mindset: you are equally comfortable architecting a solution, writing production code, creating a training deck, and facilitating a workshop.
• A deep passion for AI and a genuine desire to help others harness its potential to transform how they work.

What you’ll get:

• A fast-paced and collaborative environment
• Learning and development allowance
• Competitive compensation and opportunity for advancement
• 100% medical, dental, and vision coverage
• Up to $25K reimbursement for fertility, adoption, and parental planning services
• Flexible PTO policy
• Monthly wellness stipend, home office stipend

#LI-TD1

The opportunity 

The Autonomous Freight Systems team is a brand new, AI-first engineering team in San Francisco. This team will own Flexport’s client-facing rates platform and the self-serve freight booking experience—two of the highest-leverage surfaces in our Client App that dictate how clients see pricing and book freight without manual intervention.

As a Staff Engineer, you will be the technical anchor for our next-generation AI-powered rates platform. We aren't just building a UI; we are building AI agents that handle real logistics work: parsing complex rate sheets, managing pricing intelligence across ocean, air, and trucking, and making "Self-Serve" a reality for thousands of shippers. You will build the intelligence layer that allows clients to commit freight on technology alone, with no account executive and no operations touch.

This is a ground-floor opportunity to shape technical direction, set up a new codebase, and build the platform that moves Flexport from an assisted-sales model to a tech-run one for the long tail of our client base. You will partner with Pricing, Sales, Ops, and Design on hard domain problems and ship to the most-used client surfaces at the company.

You will

• Design and ship end-to-end AI agents that handle pricing intelligence and booking compliance without human oversight.
• Create the tooling and secure AI infrastructure required to expose internal logistics data to LLMs safely.
• Navigate the rapidly evolving landscape of agent patterns, RAG, and prompt engineering to solve problems that don’t have "Stack Overflow" answers yet.
• Build and launch products rapidly and incrementally: we ship to production dozens of times per day.
• Mentor and uplevel engineers across the team, fostering a culture of technical excellence.
• Partner with the Engineering Manager to define the team’s technical roadmap and hiring bar.

You should have

• LLM Fluency: Experience with agent patterns, RAG, and prompt engineering. You know what works in production versus what only looks good in a demo.
• Agentic Orchestration: Experience with workflow orchestration (e.g., Temporal) and building reliable systems around non-deterministic AI.
• Entrepreneurial Drive: You don't wait for a spec; you find the highest-leverage automation problems and design the AI-driven solution.
• Product Instincts: You can identify what is worth building, not just execute on a spec. You have a proven track record of building data-heavy applications where high-quality user interaction is the priority.
• Architecture Leadership: Demonstrated experience leading technical initiatives across multiple teams or systems, specifically in building unified interfaces that aggregate data from various backend systems.
• Technical Rigor: A love of simple, well-tested code that is easy to extend and hard to break, combined with experience building large-scale distributed systems.
• Technical Communication: The ability to convey technical ideas in common terms to business partners, product managers, and engineers alike.

It's a plus if you have

• Experience with elements of our tech stack: Java, Spring Boot, Ruby on Rails, React, AWS.
• Experience in logistics, supply chain, or freight technology.
• Experience building internal tools or operator-facing applications.
• A bias for action: When you see a problem, you solve it.

#LI-onsite

About the Role

We're hiring an AI Engineer to work on the AI core of Mill Commercial — the computer vision and agentic systems that turn a stream of food waste into operational intelligence for commercial kitchens. Mill Commercial integrates a camera and onboard compute directly into our high-capacity food recycler; models running on the edge identify, classify, and quantify food scraps at the point of generation, and our vision pipeline turns that signal into procurement and operational guidance for large food service operators.

You'll join a small AI team, building the data and training pipeline that produces our edge CV models, designing the cloud-side evaluation that tells us whether those models are good enough to ship, and helping build the agentic, LLM-driven product features that turn raw waste data into customer-facing insights and recommendations. This is a hands-on senior IC role for someone who's equally comfortable fine-tuning a segmentation model, prompting a VLM, and wiring an agent into a product feature.

What You'll Do

• Build and manage the end-to-end ML training pipeline: data ingestion from deployed kitchen units, ground truth generation, annotation tooling (including foundation-model-assisted labeling), training, evaluation, and retraining cycles.
• Train and evaluate segmentation, classification, and mass-estimation models for the Mill Commercial camera pipeline — from prompting foundation models to fine-tuning ConvNets and VLMs.
• Build the cloud-side evaluation harness that tells us how our shipped edge models are actually performing in the field — automated, reproducible, and aligned to product accuracy targets across food types, kitchen environments, and deployment configurations.
• Own MLOps: reproducible training, experiment tracking, model versioning, and automated evaluation against product-defined accuracy targets.
• Export and validate models for deployment to edge devices, working closely with the edge team on optimization, quantization, and integration.
• Help design and build the LLM- and agent-powered product features that consume waste characterization data and turn it into customer-facing recommendations — purchasing suggestions, anomaly explanations, operational nudges. Define how agents call tools, ground in customer data, and stay reliable in production.
• Analyze failure cases systematically — unfamiliar food classes, novel kitchen environments, challenging lighting and clutter conditions — and drive the data and modeling decisions that close accuracy gaps.

What We're Looking For

• Strong fundamentals in computer vision and deep learning — segmentation, detection, classification, tracking. You understand the architectures well enough to make informed choices.
• Fluency with modern ML approaches — VLMs, LLMs, foundation models, and agentic systems — alongside classical deep learning. You know when to fine-tune a ConvNet, when to prompt a VLM, and when to wire up an agent, and you understand the practical realities of putting any of them into a product.
• Experience building ML training pipelines and data annotation systems at scale.
• Experience evaluating ML models rigorously — designing metrics, building the eval harness, and using results to drive product decisions rather than just publish a number.
• Proficiency with cloud ML infrastructure (AWS or equivalent) — you've managed training jobs, data pipelines, and experiment workflows in production.
• Familiarity with cloud-to-edge model deployment.
• Clear, direct communication — you can explain tradeoffs to non-technical stakeholders, push back honestly when you disagree, and write docs that others can follow.
• Genuine interest in applying AI to food waste reduction and sustainability. This is a mission-driven product and we want people who care about the mission.

Software skills: Python, PyTorch, OpenCV. Strong familiarity with MLOps on AWS infrastructure. Experience with LLM and agent frameworks. Google Cloud / Gemini experience is a plus.

Nice to Have

• Experience with video understanding (temporal consistency, tracking, video segmentation)
• Experience with foundation models for data annotation
• Experience with MLOps tooling (Weights & Biases, MLflow, SageMaker, or equivalents)
• Experience shipping LLM- or agent-powered features in a consumer or B2B product
• Hardware / IoT product experience, particularly with computer vision and cameras for embedded systems

The estimated base salary range for this position is $240 to $280k, which does not include the value of benefits or a potential equity grant. A wide range of factors are considered in making compensation decisions, including but not limited to skill sets, market conditions, experience and training, licensure and certifications, and business and organizational needs. At Mill, it is not typical for an individual to be hired at or near the top of the range for their role.

Summary

We are looking for a software engineer to build internal tools that help our battery algorithm team work faster and smarter. You will develop applications for test data analysis, visualization, and documentation, incorporating AI powered capabilities where they accelerate engineering workflows. This is a software engineering role: you will write clean, maintainable code, build intuitive interfaces, and integrate modern AI services into practical tools that engineers actually use.

What You Will Do

• Build Python based applications for analyzing and visualizing large scale battery test data, including time series datasets.
• Develop web based GUIs that let engineers explore, filter, and interact with test results.
• Integrate LLM APIs (e.g., OpenAI, Anthropic) into workflows to automate documentation, extract context from requirement sheets, change reports, and code repositories.
• Create data pipelines that pull from multimodal sources and present actionable insights.
• Collaborate with algorithm engineers and test engineers to understand their pain points and build tools that solve real problems.
• Continuously improve tooling based on user feedback and evolving team needs.

Minimum Qualifications

• B.Sc. or M.Sc. in Computer Science, Electrical Engineering, Mechanical Engineering, or related field.
• 3+ years of professional software development in Python, including data analysis and visualization libraries (pandas, matplotlib, plotly, etc.)
• Experience building web based tools or applications with a frontend component (e.g., React, Streamlit, Dash, or similar)
• Strong debugging and problem solving skills.
• Comfortable working with APIs and integrating third party services.

Preferred Qualifications

• Experience integrating LLM APIs or AI services into applications (prompt engineering, API orchestration, retrieval augmented generation)
• Familiarity with time series data analysis
• Experience with document automation or generation (Word, PDF, slides)
• Exposure to battery management systems or embedded systems workflows• Experience with CI/CD, version control best practices, and collaborative development environments

At.Monks, we believe in fostering an environment where a diversity of perspectives can thrive. We proactively work to design hiring processes that promote equity and inclusion while mitigating bias. We celebrate diversity and are committed to building a team that reflects the communities we serve. We welcome and encourage qualified applicants, from all backgrounds, who are excited to contribute to our mission.  

What We Offer

.Monks has provided a compensation range that represents its good faith estimate of what Media.Monks may pay for the position at the time of posting. .Monks may ultimately pay more or less than the posted compensation range. The salary offered to the selected candidate will be determined based on job-related factors, but not based on a candidate’s sex or any other protected status.

Cupertino, CA

Snapshot 

At Google DeepMind, our research team is dedicated to tackling the most complex challenges in online information quality. We strive to advance the state of the art by developing innovative solutions to detect manipulated media and misleading narratives, ensuring the integrity of digital discourse. A prominent example of our scientific discovery is Backstory. Our interdisciplinary work spans provenance analysis and the creation of tools for AI-assisted information literacy, leveraging our technologies for the widespread public benefit of a safer online environment. We thrive in a supportive environment that encourages rapid prototyping and iteration, driving our research achievements directly into Google’s flagship models, including Gemini.

About us 

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

We’re a dedicated scientific community, committed to “solving intelligence” and ensuring our technology is used for widespread public benefit. 

We’ve built a supportive and inclusive environment where collaboration is encouraged and learning is shared freely. We don’t set limits based on what others think is possible or impossible. We drive ourselves and inspire each other to push boundaries and achieve ambitious goals.

The role

To succeed in this role, you will need to be passionate about advancing information literacy using machine learning and other computational techniques. You'll join an interdisciplinary team of domain experts, ML researchers, and engineers to research and build multimodal reasoning systems and Vision-Language Models (VLMs) to assess the trustworthiness of media (images, audio, and videos) on the internet.   

Key responsibilities:

• Plan and perform rapid prototyping of computer vision and multimodal machine learning techniques applied to determining authenticity of media information.
• Design and train multimodal models capable of complex visual reasoning.
• Undertake exploratory analysis to inform experimentation and research directions.
• Engage with product teams to drive the development of our research.
• Implement tools, libraries, and frameworks to speed up and enable new research.
• Report and present research findings, software developments, experimental results, and data analysis clearly and efficiently.
• Collaborate with internal and external scientific domain experts.

About you

In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:

• PhD/Master’s degree in Computer Science, AI, ML, or equivalent practical experience.
• At least 2 years of relevant experience developing computer vision techniques or multimodal machine learning models.
• Experience in software development using Python and deep learning frameworks (e.g., Jax, TensorFlow, PyTorch), with a proven track record of building high-quality research prototypes and systems.
• Quantitative skills in math and statistics.
• Experience exploring, analysing and visualising data.

In addition, the following would be an advantage: 

• Experience in training and deployment of large-scale models.
• Experience with Video Understanding
• Experience with Large Language Models, prompt engineering, few-shot learning, post-training techniques, and evaluations.
• A proven track record of research or engineering achievements, such as publications in peer-reviewed conferences or journals.

When assessing technical background we will take a holistic view of the mix of scientific, ML and computational experience. We do not expect you to be an expert in all fields simultaneously.

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

The US base salary range for this full-time position is between 174,000 USD - 252,000 USD + bonus + equity + benefits. Your recruiter can share more about the specific salary range for your targeted location during the hiring process.

THE ROLE

Everpure is seeking a talented and experienced Agentic AI Software Engineer to join our dynamic team in Santa Clara, CA. In this role, you will build production-grade agentic AI solutions that drive enterprise automation and digital transformation. You will design and deploy LLM-based services , implement strong prompt engineering techniques, guardrails, and model evaluation frameworks, and ensure secure, scalable delivery on AWS. This role focuses on building reliable, secure, and observable AI systems integrated with enterprise platforms.

WHAT YOU'LL DO

• Design and develop production-grade Agentic AI solutions using LLMs, multi-agent workflows, and RAG pipelines.
• Implement and refine Prompt Engineering strategies, including structured prompts, tool-calling, and agent orchestration to ensure reliable and deterministic AI behavior.
• Build secure backend services and REST APIs in Python, integrating AI agents with enterprise systems using microservices architecture.
• Establish robust guardrails, including output validation, fallback strategies, rate limiting, safe tool invocation, and human-in-the-loop patterns where appropriate.
• Develop and maintain model evaluation frameworks, including automated prompt testing, retrieval validation, regression testing, and performance benchmarking.
• Deploy and operate AI services on AWS, leveraging services such as EC2, S3, Lambda, RDS, and containerization using Docker and Kubernetes.
• Apply strong security best practices, including access control, data protection, audit logging, and secure API design.
• Ensure strong observability, monitoring, and reliability of AI-driven services in production.
• We are primarily an in-office environment and therefore, you will be expected to work from the Santa Clara, CA office in compliance with Everpure's policies, unless you are on PTO, or work travel, or other approved leave.

WHAT YOU BRING

• 3–6 years of professional software engineering experience building backend or distributed systems.
• Strong proficiency in Python and developing RESTful APIs.
• Hands-on experience building LLM-based or Agentic AI applications, including RAG, embeddings, and vector database integrations.
• Practical experience in Prompt Engineering and understanding of LLM behavior, limitations, and optimization techniques.
• Experience deploying and operating applications in AWS cloud environments.
• Experience implementing AI guardrails, model validation techniques, and production monitoring.
• Solid understanding of cloud-native security practices and secure system design.

Preferred Qualifications

• Experience with multi-agent orchestration frameworks or AI tool integration patterns.
• Familiarity with vector databases such as Pinecone, Weaviate, OpenSearch, or similar.
• Experience with CI/CD pipelines and containerized deployments using Docker and Kubernetes.
• Exposure to enterprise platform integrations such as ServiceNow, Snowflake, or similar systems.
• Familiarity with observability tools such as Datadog, Prometheus, or similar platforms.

#LI-ONSITE

#LI-TH3

THE ROLE

Everpure is seeking a talented and experienced Agentic AI Software Engineer to join our dynamic team in Santa Clara, CA. In this role, you will build production-grade agentic AI solutions that drive enterprise automation and digital transformation. You will design and deploy LLM-based services , implement strong prompt engineering techniques, guardrails, and model evaluation frameworks, and ensure secure, scalable delivery on AWS. This role focuses on building reliable, secure, and observable AI systems integrated with enterprise platforms.

WHAT YOU'LL DO

• Design and develop production-grade Agentic AI solutions using LLMs, multi-agent workflows, and RAG pipelines.
• Implement and refine Prompt Engineering strategies, including structured prompts, tool-calling, and agent orchestration to ensure reliable and deterministic AI behavior.
• Build secure backend services and REST APIs in Python, integrating AI agents with enterprise systems using microservices architecture.
• Establish robust guardrails, including output validation, fallback strategies, rate limiting, safe tool invocation, and human-in-the-loop patterns where appropriate.
• Develop and maintain model evaluation frameworks, including automated prompt testing, retrieval validation, regression testing, and performance benchmarking.
• Deploy and operate AI services on AWS, leveraging services such as EC2, S3, Lambda, RDS, and containerization using Docker and Kubernetes.
• Apply strong security best practices, including access control, data protection, audit logging, and secure API design.
• Ensure strong observability, monitoring, and reliability of AI-driven services in production.
• We are primarily an in-office environment and therefore, you will be expected to work from the Santa Clara, CA office in compliance with Everpure's policies, unless you are on PTO, or work travel, or other approved leave.

WHAT YOU BRING

• 3–6 years of professional software engineering experience building backend or distributed systems.
• Strong proficiency in Python and developing RESTful APIs.
• Hands-on experience building LLM-based or Agentic AI applications, including RAG, embeddings, and vector database integrations.
• Practical experience in Prompt Engineering and understanding of LLM behavior, limitations, and optimization techniques.
• Experience deploying and operating applications in AWS cloud environments.
• Experience implementing AI guardrails, model validation techniques, and production monitoring.
• Solid understanding of cloud-native security practices and secure system design.

Preferred Qualifications

• Experience with multi-agent orchestration frameworks or AI tool integration patterns.
• Familiarity with vector databases such as Pinecone, Weaviate, OpenSearch, or similar.
• Experience with CI/CD pipelines and containerized deployments using Docker and Kubernetes.
• Exposure to enterprise platform integrations such as ServiceNow, Snowflake, or similar systems.
• Familiarity with observability tools such as Datadog, Prometheus, or similar platforms.

#LI-ONSITE 

#LI-TH3

The Impact You’ll Be Contributing to Moloco:

• We're seeking a software engineer to help us design, develop and maintain the backend of our state of the art Moloco Commerce Media (MCM) product
• You will be responsible for developing an ML-based online advertising platform for the rapidly growing retail media industry
• You will play a pivotal role building and developing our enterprise-ready solution. We’ve determined clear market fit and are now seeking senior and staff engineers to refine the MCM product
• You will lead feature development, design, optimization, testing, and maintenance of software applications and solutions for internal and external end users
• You will mentor others on the team and have the opportunity to lead high-impact projects

The Opportunity:

• Design and ship agentic software that helps advertisers manage campaigns through natural language interfaces and intelligent automation
• Build customer-facing applications that combine LLM capabilities with robust backend systems—from conversational campaign creation to AI-assisted performance insights
• Own the full stack: React/TypeScript frontends, Go/Python/Java services, and the infrastructure that connects them
• Collaborate closely with PM, data science, and partner teams to shape product direction and turn ambiguous goals into shipped features
• Operate what you build: dashboards, alerts, runbooks, and a sustainable on-call rotation
• Mentor engineers through code reviews, pairing, and design discussions
• Prototype and explore what's next—advanced analytics, creative-level reporting, and AI capabilities we haven't imagined yet

How Do I Know if the Role is Right For Me?

• 5+ years of software engineering experience
• Full-stack development experience—you're comfortable across the frontend, backend, and the glue in between
• Hands-on experience building LLM-based applications (agents, tool use, prompt engineering, or similar)
• System design and development skills; from gathering requirements to design to implementation to production
• Experience in Cloud/Big Data platforms (e.g., AWS, GCE, GCP, Beam, Spark, BigTable, BigQuery etc.)
• Programming fundamentals, testing, and common algorithms and structures
• Track record of shipping customer-facing products
• Growth mindset—you stay current on emerging technologies and enjoy sharing knowledge with others
• Excellent communication and collaboration skills

The Role

As an Applied AI Marketing Engineer, you will be a core, hands-on builder within the new Marketing AI Spoke team. You will function as the primary product owner and technical implementer for a specific portfolio of AI agents, workflows, and low-code/no-code applications. This role requires a blend of marketing acumen, technical aptitude, and problem-solving skills to translate complex business challenges into tangible, deployed, AI-driven solutions.

You will be responsible for the full engineering lifecycle—defining, developing, testing, and deploying high-impact initiatives that directly improve our marketing productivity, operational efficiency, and buyer experiences.

Responsibilities

• Marketing AI Solution Development: Patterning with TDI leveraging company-wide approved platform. Design, build, and maintain the underlying infrastructure and services required to run scalable AI applications for the Marketing organization. This includes connecting data sources, building APIs, developing front-end user interfaces (UI/UX), and engineering the backend logic.
• AI Agent & Workflow Engineering: Architect, code, test, and deploy AI agents and low-code/no-code workflows that solve critical marketing challenges. Ensure solutions are robust, secure, and seamlessly integrated into the existing AI stack (e.g., CRM, MAP, CMS).
• Agentic Strategy & Architectural Design: Act as the technical leader for your assigned portfolio, defining the architectural blueprints for new AI enabled workflows. Evaluate and recommend the appropriate technical tooling (e.g., cloud services, databases, low-code platforms) to maximize performance and minimize operational costs.
• Data and Prompt Engineering: Partner with data science and marketing ops teams to ensure data pipelines support AI application needs (RAG, fine-tuning). Design and iterate on effective prompts and input-output schemas to optimize the performance and reliability of large language model (LLM) agents.
• Value Measurement & System Optimization: Implement performance tracking directly into deployed platforms (instrumentation). Use technical metrics (latency, uptime, error rates) and business metrics (ROI, time saved) to justify system improvements and guide the marketing AI roadmap. 

What You'll Bring

• A Bias for Action and Iteration: Demonstrated passion for a test-and-learn philosophy, quickly prototyping and validating solutions to drive continuous improvement.
• AI-First Technology Mindset: Deep curiosity and commitment to leveraging AI and machine learning as foundational elements for building next-generation marketing tools and operational workflows.
• Marketing Domain Curiosity: A strong interest in marketing dynamics, customer behavior, and business outcomes, translating technical capabilities into tangible marketing value.
• Preferred Experience (A Strong Plus): Prior background in marketing operations, strategy, or experience working on tools/platforms utilized by large-scale marketing organizations.

Why You'll Love This Role

• Become the Architect of Change: You won't be maintaining existing systems; you will be building a new, high-impact function from the ground up. This is a chance for true architectural ownership over the next generation of our marketing operating model.
• Tangible and Immediate Impact: Your work on groundbreaking AI projects will have a direct, measurable impact on our business performance, customer experience, and efficiency. You will see your innovations adopted by hundreds of marketers and contribute to trillions in potential value.
• High-Visibility, Strategic Ownership: This role offers executive-level visibility as you directly shape the future of marketing strategy at Okta, moving the function from manual repetition to hyper-efficient, data-driven automation.

#LI-Hybrid
PID: P24412_3380938

The Auth0 Team

Auth0 is an easy-to-implement authentication and authorization platform designed by developers for developers.  We make access to applications safe, secure, and seamless for the more than 100 million daily logins around the world. Our modern approach to identity enables this Tier-Ø global service to deliver convenience, privacy, and security so customers can focus on innovation.

This team focuses on providing tenant-level protections to our customers, at scale. From bot detection to brute-force to suspicious IP throttling and beyond, this team often provides the first line of defense for Auth0 customers.

The Staff Software Engineer 

At Okta, we’re building the next generation of authentication for the GenAI era. 

We’re looking for a Staff Software Engineer to join the AI DevEx team at Auth0. This role is pivotal in extending and complementing our Auth for GenAI offering by building the infrastructure, tooling, and developer experiences that empower both human developers and AI agents to build secure, intelligent applications.

Auth0 Emerging Tech is the Engineering organization where we take care of the hottest technology out there: we ship fast, we don't break things. We are a dynamic and collaborative distributed and diverse team. We value ownership, learning and innovation.  

This is an ideal role for an engineer who enjoys building for other engineers, working across stacks, and shaping the future of AI enablement in production systems. You'll collaborate across engineering, product, and security teams to drive meaningful improvements in developer tooling, agent authentication, orchestration frameworks, and real-world demos.

What you will be doing:

• Design and Build Developer Tooling that helps developers secure and manage infrastructure like MCP servers
• Build Demo Applications that showcase secure, identity-powered AI use cases in real-world environments
• Contribute to Open Source Projects, both within Auth0 and across the broader AI + identity ecosystem
• Write and Maintain High-Quality Documentation including API references, quickstarts, and best practices for both developers and AI-native tooling (e.g., llm.txt)
• Drive Integration with Emerging AI Frameworks by creating adapters, utilities, and interfaces for agent runtimes and orchestration layers
• Collaborate with Design, Product, and Security teams to align on developer needs, roadmap direction, and compliance requirements
• Mentor and Support Other Engineers, setting strong examples in code quality, testing practices, and architectural thinking
• Influence Engineering Standards by leading design discussions and contributing to team-wide architectural decisions
• Ensure Resilience and Security of systems involved in agent-to-agent or model-to-service communication

You Might Be a Good Fit If You

• Experience in software engineering with a proven track record in building tools, frameworks, or platforms for other developers
• Proficiency in JavaScript/TypeScript, Golang and/or Python, and the ability to move fluidly between front-end and back-end contexts
• Experience working with LLM APIs, agent runtimes, orchestration layers, or prompt pipelines
• Familiarity with authentication and authorization systems, especially standards like OAuth2, OIDC, and JWT
• Demonstrated experience leading architecture and design efforts for scalable, production-grade systems
• Comfort contributing to and maintaining open source projects and engaging with developer communities
• A passion for documentation as part of the developer experience—not just writing code, but making it understandable and usable
• Ability to thrive in highly collaborative environments with cross-functional stakeholders

Technologies You May Work With

• Languages: JavaScript, TypeScript, Python
• Frameworks: React, Next.js, FastAPI
• AI Ecosystem: Model APIs, orchestration runtimes, prompt management systems, agent toolkits
• Auth0 Stack: Token Vault, Async Authorization, Fine-Grained Authorization (FGA)

#LI-Hybrid

P23578_3268067

AI Engineer
Location: San Francisco, CA, in-office
Compensation: 150,000 – $210,000
Company: Cargomatic, Inc.

Disruptive, groundbreaking, transformative company is changing an industry

Join a rapidly growing company disrupting the trucking industry! Cargomatic is the #1 technology platform and digital marketplace for powering world-class, local trucking. Take a look around you. Literally everything humans build, grow, or sell has spent time on a truck. Local trucking is the lifeblood of every regional economy, and yet this $82 billion industry still relies heavily on phone calls and fax machines. Cargomatic is transforming the way goods move around every local node in the supply chain by connecting shippers and commercial truck drivers with mobile technology. We are solving complex, real-world problems every day, and giving full transparency to the shipping process. 

AI Engineer- Rapid Prototyping & Automation

Cargomatic is seeking a highly hands-on AI Engineer who thrives in fast-paced environments and is passionate about rapid prototyping and deploying standalone AI-driven applications. This role focuses on building agentic AI systems and automation tools that can operate independently, integrate with our ecosystem, and deliver immediate business value.

You will work at the intersection of AI, product development, and infrastructure- quickly turning ideas into working prototypes and scaling the most promising solutions into production.

Key Responsibilities:

• Rapidly design, prototype, and deploy standalone AI applications that solve real-world logistics and operational challenges
• Build and implement agentic AI systems capable of autonomous decision-making and task execution
• Develop AI-powered automation tools to streamline workflows across operations, customer experience, and internal processes
• Leverage modern LLM ecosystems, including Claude-based and code-first AI frameworks, to build intelligent agents and workflows
• Integrate LLMs, APIs, and data pipelines into production-ready services
• Collaborate with product, engineering, and operations teams to identify high-impact opportunities for AI solutions
• Iterate quickly based on feedback, performance metrics, and evolving business needs
• Ensure scalability, reliability, and security of AI systems in production environments
• Stay current with advancements in AI/ML, especially in agent frameworks, Claude/OpenAI ecosystems, and automation tooling (e.g., n8n)
• Implement guardrails, validation layers, monitoring, and evaluation frameworks to mitigate hallucination, prompt injection, and data security risks.
• Collaborate cross-functionally with Product, Engineering, and Operations to translate business needs into scalable AI-enabled solutions.
• Contribute to architectural standards, technical best practices, and innovation initiatives.

Preferences:

• Strong product builder mindset with an ability to prioritize high-impact solutions
• 3+ years of experience in software engineering, with a focus on AI/ML or automation
• Proven experience building and deploying AI-powered applications or prototypes end-to-end
• Strong understanding of agentic AI concepts, LLMs, and modern AI frameworks
• Hands-on experience with Claude (Anthropic), OpenAI, or similar LLM APIs, including prompt design and orchestration
• Experience with code-based AI frameworks (e.g., Claude tool use / function calling, LangChain, AutoGen, CrewAI, or similar)
• Experience building or orchestrating workflows using n8n (or similar tools like Zapier, Temporal, Airflow)
• Experience with event-driven architectures and real-time systems
• Familiarity with vector databases and retrieval-augmented generation (RAG)
• Proficiency in backend development using Node.js
• Experience with frontend frameworks such as React for building lightweight interfaces
• Hands-on experience with cloud infrastructure, particularly AWS (Lambda, ECS, S3, etc.)
• Familiarity with databases such as MongoDB and RDS (PostgreSQL/MySQL)
• Experience integrating third-party APIs and building microservices
• Ability to move quickly from concept → prototype → production
• Active GitHub contributions or demonstrated passion for emerging technologies in AI and data systems.

Benefits & Perks:

• Competitive compensation
• Medical, dental, and vision benefits
• 401K company match program
• Flexible paid time off (PTO) and paid holiday

About the team/role

We’re hiring an ML Engineer (P2) to build and ship the AI systems that power Checkr’s core products. This role sits on the ML team inside Checkr’s Data & ML organization within Engineering.

Checkr runs millions of background checks a year. The ML team builds the systems that make those checks faster, more accurate, and cheaper to operate: document processing, charge classification, entity resolution, and in-product intelligence. These are production services that Product Engineering depends on daily.

This is not a research role or a notebook role. You’ll own ML services end-to-end: design them, code them, deploy them, monitor them. We need someone who writes production software, builds with LLMs and APIs as first-class tools, and can tell the difference between working code and AI slop. If you’ve spent the last few years building AI-native software and you care deeply about engineering craft, we want to talk.

This role sits in the central Data & ML team within the Engineering organization. You will partner daily with Product Engineering, Product, and cross-functional teams. You’ll also contribute to Checkr’s broader AI strategy, including our initiative to deploy our agentic fleet and build scalable context with our semantic layer.

We are looking for someone based in San Francisco who has built ML systems in fast-moving, impact-first environments. Less process, more shipping. Less paperwork, more results.

What you’ll do

• Build and deploy ML/AI services. Design, develop, and ship ML models and AI systems that Product Engineering teams rely on. You write the model code, the API layer, the monitoring, and the tests. Not notebooks; production services.
• Design with LLMs and APIs. Use LLM APIs (OpenAI, Anthropic, etc.) as building blocks in production systems. You know when to call an LLM, when to fine-tune, when to use a classical model, and when to write a rule. You think about cost, latency, and quality together.
• Ship production software. Write clean, well-structured code with solid OOP, proper abstractions, error handling, and tests. Your code gets reviewed by SWEs and passes. CI/CD is how you work, not something you bolt on at the end.
• Partner with product and engineering. Translate business problems into ML solutions. Define API contracts with product engineers. Explain your approach clearly to non-ML partners and leave the room with alignment, not confusion.
• Evaluate and iterate fast. Build evaluation frameworks, run experiments, and make data-driven decisions about model and system performance. Ship and iterate; don’t wait for perfect.
• Ship AI-powered workflows. Put AI to work on your own processes: automate pipelines, build agentic workflows, and contribute reusable skills and context to Checkr’s agentic platform. The expectation is that our teams operate AI-first.

What you bring

• A Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field, or equivalent depth from experience
• 4+ years building software professionally, with at least 2 of those building ML systems that run in production
• Strong Python fluency; you write clean, testable, well-structured code with solid OOP instincts. Not scripts; software
• Hands-on experience using LLM APIs in production systems: prompt engineering, structured outputs, function calling, cost management, and evaluation
• You’ve built and maintained APIs, worked with CI/CD pipelines, and shipped code that other engineers depend on
• Comfortable with distributed systems concepts: queues, async processing, caching, horizontal scaling
• Experience with NLP tasks in production: classification, extraction, entity resolution, summarization
• Comfort with and enthusiasm for AI-assisted workflows; experience using LLMs, code-generation tools, or agentic systems in production or operational contexts is a strong signal
• You can evaluate tradeoffs: fine-tune vs. prompt, hosted vs. self-deployed, classical ML vs. LLM, rule vs. model
• Strong communication skills; you explain technical decisions clearly to engineers and non-engineers alike, without hiding behind jargon
• You use AI tools (Copilot, Claude, etc.) to move faster, but you understand every line they produce. You can spot AI slop and you don’t ship it
• An A-player mindset with a strong bias for action: you raise the bar, move with urgency, stay resilient through ambiguity, and take ownership to deliver meaningful outcomes.

Nice to have

• Experience with MLOps platforms (MLflow, SageMaker, Vertex, or similar)
• Background in document processing, OCR, or information extraction
• Experience with PySpark or large-scale data processing
• Ruby experience (Checkr’s platform runs on Rails)
• Familiarity with compliance-sensitive domains (fintech, legal tech, HR tech)
• Working knowledge of dbt, Snowflake, or modern ELT/data transformation tools

What you’ll get

• A fast-paced, low-bureaucracy environment where shipping matters more than process
• Direct exposure to executive and C-level decision-making
• A seat on the Data & ML team shaping Checkr’s AI strategy
• Learning and development allowance
• Competitive cash and equity compensation and opportunity for advancement
• 100% medical, dental, and vision coverage
• Up to $25K reimbursement for fertility, adoption, and parental planning services
• Flexible PTO policy
• Monthly wellness stipend

Anduril is developing and deploying an integrated system to make UxVs highly effective through the entire mission lifecycle. From fleet management, to mission planning, live execution, and integrations into other DOD systems, Lattice is enabling the military to fight at scale with robots across all domains. Anduril is leveraging AI across all aspects of this lifecycle to reduce the barrier to entry for new operators, and increase the scale at which operators can leverage Lattice.

Your Job is to design, implement, and evolve the means by which Agents interact with classical software systems. You will contribute to test and eval infrastructure, deployment software, agent tool calling, prompt engineering, and other agent techniques in order to achieve best-in-class performance with frontier and edge deployed models. You will increase the performance and quality of agentic interaction patterns, and you will expand the surface area by which Agents can interact with the overall Lattice system. - good to ship

Required Qualifications

• Demonstrated deep knowledge of the implementation and use of LLM’s
• Experience integrating agents into classical systems 
• Strong programming skills in Python or other core languages (Java, Go etc)
• PhD or Master’s degree in Computer Science, Robotics, Machine Learning, or a related field, or equivalent practical experience.

Preferred Qualifications

• Experience with simulation or real-world validation for autonomous vehicles is highly desirable
• Novel application track record and experience including first author publications, participation in peer reviewed conferences, contribution to open source projects, and demonstrated contribution to the ML and AI community.
• Experience in multi-modal sensor data processing (e.g., cameras, LiDAR, radar).
• Familiarity with ML Ops best practices, including model versioning and reproducible research pipelines.
• Familiarity with C/C++ is a plus.
• General software engineering experience solving motion planning or related robotics problems.

ABOUT THE TEAM:

The Developer Platform team builds the tools and infrastructure that power software development at Anduril. We own the company's primary developer CLI (2,300+ engineers, 93k+ invocations), CI/CD pipelines serving 3,500+ engineers across 23 product lines in the US, Australia, and UK, deployment and release management systems, and Anduril's LLM adoption platform. We are standing up a new agentic development initiative to build the next generation of AI-powered developer infrastructure. The team is growing fast and this is a ground-floor opportunity to define what agentic software development looks like at a company building mission-critical defense systems.

WHAT YOU'LL DO:

• Build multi-agent orchestration infrastructure that moves AI coding agents from individual developer machines to servers executing work autonomously across repositories at company scale
• Design and implement an agent-optimized, real-time knowledge base that gives AI agents deep context on codebases, architecture, and operational patterns
• Build a skills and plugin platform that lets any team encode their domain knowledge into reusable AI capabilities
• Improve CI/CD reliability and developer experience across 23 product lines
• Create release tooling that simplifies shipping software to production environments and robotic platforms
• Operate and evolve Anduril's LLM adoption platform: model rollouts, capacity management, cost attribution, and developer onboarding

REQUIRED QUALIFICATIONS:

• Strong software engineering fundamentals in one or more of: Go, Python, TypeScript
• An entrepreneur, builder, PM, and designer rolled into one. We need people with outstanding software taste who can identify the right problem, design the solution, build it, and ship it in days, not quarters.
• A passion for developer tooling. You notice friction in your own workflow and cannot help but fix it. You spend 4 hours sharpening the axe and 30 minutes chopping wood.
• Demonstrated ability to quickly learn and adapt to unfamiliar technologies across multiple domains
• Must be a U.S. Person due to required access to U.S. export controlled information or facilities

PREFERRED QUALIFICATIONS:

• Experience building developer tools, CLIs, CI/CD systems, or platform infrastructure
• Experience with Kubernetes, Nix, or cloud infrastructure (AWS/Azure)
• Genuine interest in LLMs and agentic systems as applied to developer productivity. If you are already building with AI coding agents in your daily workflow, you will feel right at home.
• Prior work on LLM integrations, prompt engineering, or agent frameworks

The Product Solutions Architecture (PSA) team acts as a technical multiplier across Datadog. PSAs are domain experts who partner with Field teams on complex customer use cases across pre- and post-sales engagements and scale their impact by producing reusable collateral, including reference architectures, technical guides, and enablement assets. By feeding real-world customer insights back to Datadog Product teams, PSAs help influence product roadmaps while accelerating adoption, usage, and long-term customer success.

Datadog’s LLM Observability product enables organizations to monitor, troubleshoot, and optimize large-scale LLM-powered applications with confidence, while meeting requirements around data privacy, compliance, and cost management. As a Product Solutions Architect, you will partner closely with Datadog customers and the LLM Observability product team to design architectures, implement best practices, and drive adoption of LLM observability across customer environments.

At Datadog, we place value in our office culture - the relationships and collaboration it builds, and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.

What You’ll Do:

• Serve as the in-house subject matter expert for Datadog’s LLM Observability product
• Partner with Field teams to provide hands-on technical and architectural guidance to enterprise customers adopting LLM Observability
• Create high-impact technical collateral, including reference architectures, technical guides, cookbooks, and documentation to enable Field teams and the broader customer community
• Build proofs of concept and small-scale deployments to validate solutions and reproduce real-world customer environments
• Act as a trusted advisor to Product Management by delivering actionable feedback informed by real-world field experience

Who You Are: 

• You bring a strong software engineering foundation, with hands-on experience building and operating LLM-powered applications in production environments, and are comfortable diving deep into codebases to understand architectural decisions, tradeoffs, and implementation details
• You are familiar with distributed systems and core observability concepts, such as tracing, metrics, and logging, and can translate those patterns into the emerging LLM observability domain, including LLM spans, evaluations, and prompt templates
• You have experience with languages such as Python and/or JavaScript/TypeScript
• You are comfortable operating in rapidly evolving, ambiguous technical domains
• You build deep context across teams and translate it into reusable, scalable solutions
• You take ownership from problem definition through implementation and measurable outcomes
• You are highly detail-oriented, particularly when working on architectures and customer-facing technical assets
• You bring strong listening and consultative skills and are experienced supporting customers in high-pressure, complex situations
• Able to sit up to 4 hours, traveling to and from client sites 
• Able to travel via auto, train, or air up to 40% of the time 

Bonus Points:

• Successful track record with 5+ years experience working as an Architect or Consultant 
• Experience using Datadog and/or other observability tools in an SRE or DevOps capacity
• Experience with LLM application evaluation and testing frameworks

Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply.

Benefits and Growth: 

• Best-in-breed onboarding
• Generous global benefits
• Intra-departmental mentor and buddy program for in-house networking
• New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
• Continuous professional development, product training, and career pathing
• An inclusive company culture, able to join our Community Guilds and Inclusion Talks

Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.

Who we are

The real world is the next frontier, and at Metropolis, we are creating the artificial intelligence to make it responsive. We are pioneering the Recognition Economy — a future where mundane repetition disappears and being known unlocks access, comfort, and belonging everywhere you go. From transforming parking into a seamless drive-in, drive-out experience for millions of Members to expanding our intelligence layer across retail and hospitality, we are building a world that feels instinctive and magical. The future isn’t coming; it’s here, and we need builders, innovators, and problem solvers to help us create it.

Who you are

Metropolis is seeking a Senior AI Engineer to join our Applied AI organization, a team purpose-built to rebuild how the company operates from an AI-first perspective. You will be the builder at the heart of this transformation, designing and shipping AI-powered tools and automation pipelines that replace manual, repetitive work across finance, operations, revenue, and beyond. This is a hands-on engineering role with enormous scope: you will own systems end-to-end, from the LLM prompt layer through to production deployment, working side-by-side with Process Analysts who understand the business deeply. If you want to see your work directly change how a company functions — not someday, but now — this is the role.

What you'll do

• Design, build, and ship AI-powered automation tools and internal applications that transform manual business processes into AI-first workflows
• Architect and maintain LLM-based systems, including prompt pipelines, agentic workflows, tool-calling integrations, and retrieval-augmented generation (RAG) setups
• Build and maintain data pipelines that connect source systems (Snowflake, internal APIs, SaaS tools) to AI workflows, ensuring data quality and reliability
• Partner with Process Analysts to understand how business functions work today, translate requirements into engineering specifications, and iterate based on real user feedback
• Own production reliability for the systems you build — monitoring, alerting, and iterating on AI outputs to maintain quality over time
• Evaluate and integrate emerging AI tooling (models, frameworks, APIs) to ensure Metropolis is always leveraging the best available capabilities
• Contribute to internal standards for AI development: prompt management, evaluation frameworks, model selection, and system architecture

What we're looking for

• 6+ years of software engineering experience, with 2+ years of hands-on experience designing, building, and deploying LLM-based or AI-powered applications in production
• Strong Python skills — you write clean, maintainable code and are comfortable building APIs, data pipelines, and backend services
• Practical experience with LLM APIs (OpenAI, Anthropic, or similar), prompt engineering, and agentic patterns (function calling, multi-step reasoning, tool use)
• Familiarity with data engineering fundamentals: SQL, data transformation, working with cloud data warehouses (Snowflake preferred)
• Experience with workflow orchestration tools (e.g., Airflow, Prefect, or similar) and cloud infrastructure (AWS preferred)
• A strong instinct for product quality — you care about whether the tools you build are actually useful, not just technically functional
• Ability to work autonomously in a fast-moving environment, manage competing priorities, and ship iteratively rather than waiting for perfection

While not required, these are a plus:

• Experience with vector databases, embeddings, or semantic search is a plus

Our Stack

• Languages + Frameworks: TypeScript, React, Scala (principally), Java (limited)
• Datastores: MySQL, PostgreSQL, Snowflake
• Cloud: AWS
• Version control: Git & GitHub
• AI Tooling: Copilot on GitHub and Claude Code
• Observability: Datadog

4 Days in Office: Metropolis values in-person collaboration to drive innovation, strengthen culture, and enhance the Member experience. Our corporate team members hold to our office-first model, which requires employees to be on-site at least four days a week, fostering organic interactions that spark creativity and connection

When you join Metropolis, you'll join a team of world-class product leaders and engineers, building an ecosystem of technologies at the intersection of parking, mobility, and real estate. Our goal is to build an inclusive culture where everyone has a voice and the best idea wins. You will play a key role in building and maintaining this culture as our organization grows. The anticipated base salary for this position is $170,000.00 USD to $200,000.00 USD annually. The actual base salary offered is determined by a number of variables, including, as appropriate, the applicant's qualifications for the position, years of relevant experience, distinctive skills, level of education attained, certifications or other professional licenses held, and the location of residence and/or place of employment. Base salary is one component of Metropolis' total compensation package, which may also include access to or eligibility for healthcare benefits, a 401(k) plan, short-term and long-term disability coverage, basic life insurance, a lucrative stock option plan, bonus plans, and more. #LI-CM1 #LI-Onsite

About the role

ID.me is looking for an AI Creative Designer to join our Design Team and shape how we create visual content for the communities we serve — from military members and first responders to nurses, teachers, and beyond. This role combines the eye of a photographer, the instinct of a marketer, and the mind of an engineer to produce photorealistic imagery and visual assets that connect with diverse audiences and inspire action.

You'll use the latest generative AI tools to create hero images, email visuals, personalized ads, social content, illustrations, and eventually video. But this role goes beyond producing individual assets. You'll also write code and build the systems that make personalized, on-brand visual content creation scalable — so that the right image reaches the right audience at the right time.

The ideal candidate has a strong creative portfolio, a photography background, and technical proficiency in automation, scripting, and building generation workflows

Responsibilities

• Create photorealistic images and visual assets using generative AI tools (Nano Banana, ChatGPT/DALL-E, Flux, Seedream, Runway, Seedance, and emerging platforms) for landing pages, emails, digital ads, and social media.
• Apply deep knowledge of photography fundamentals — focal length, aperture, depth of field, lighting, lens characteristics — to craft prompts that produce imagery indistinguishable from professional photography.
• Work both independently and alongside Marketing, Design, and Product team members, communicating creative vision clearly and aligning output with business goals.
• Architect systems that enable hyper-personalized visual content, adapting imagery to specific audience segments, demographics, and campaign contexts.
• Stay ahead of the rapidly evolving AI landscape, continuously evaluating and integrating new tools, models, and techniques.
• Develop and maintain prompt libraries, style references, and brand parameters to ensure visual consistency across all AI-generated content.
• Partner with Marketing, Design, and Product teams to translate campaign objectives, audience insights, and brand guidelines into visual concepts that communicate clearly and drive engagement.
• Support future expansion into AI-assisted short video creation.

Basic Qualifications

• 3+ years of experience coding, with ability sufficient to build generation systems, automation pipelines, or API integrations (Python, JavaScript, REST APIs, or equivalent).
• 2+ years of experience with generative AI image creation tools such as Nano Banana, ChatGPT/DALL-E, Flux, Seedream, Runway, Seedance, or other emerging platforms.
• 2+ years of hands-on photography experience with working knowledge of camera settings, lighting, composition, and the technical details that make images feel authentic.
• 2+ years of experience with personalized asset generation systems or dynamic content pipelines such as Contentful or Braze.
• A portfolio that includes: AI-generated photorealistic visual assets, traditional photography shot with a camera, websites or applications you've coded, and marketing campaigns you've created or contributed to.
• Bachelor's degree in Design, Photography, Computer Science, Marketing, or related field — or equivalent practical experience.

Preferred Qualifications

• Experience creating visual content for marketing campaigns, including landing pages, emails, and digital advertising.
• Strong understanding of visual storytelling and marketing principles.
• Ability to work independently and cross-functionally.
• Familiarity with the design tool Figma.
• Experience building automated workflows using APIs, webhooks, or pipeline orchestration tools.
• Background in or exposure to communities such as military, healthcare workers, teachers, or first responders.
• Understanding of A/B testing, personalization strategies, and audience segmentation.
• Experience with AI video generation tools (Runway, Seedance, or similar).

What The Role Is

We are seeking a talented Senior Data Engineer to design, build, and maintain our data infrastructure supporting mission-critical energy operations. You'll work at the intersection of renewable energy and data, developing pipelines that process everything from real-time asset performance data to complex trading and risk analytics. This hybrid role offers the opportunity to make a direct impact on clean energy operations while working with a cutting-edge data stack including Snowflake, Dagster, dbt, Modal, and GitLab.

What You'll Be Doing

Analytics Infrastructure & Data Warehouse Management

• Design, deploy, and maintain scalable data infrastructure to support enterprise analytics and reporting needs
• Manage Snowflake instances, including performance tuning, security configuration, and capacity planning for growing data volumes
• Optimize query performance and resource utilization to control costs and improve processing speed

Data Pipeline Development & Orchestration

• Build and orchestrate complex ETL/ELT workflows using Dagster to ensure reliable, automated data processing for asset management and energy trading
• Develop robust data pipelines that handle high-volume, time-sensitive energy market data and asset generation and performance metrics
• Implement workflow automation and dependency management for critical business operations

Data Transformation & Analytics Support

• Develop and maintain dbt models to transform raw data into business-ready analytical datasets and dimensional models
• Create efficient SQL-based transformations for complex energy market calculations and asset performance metrics
• Support advanced analytics initiatives through proper data preparation and feature engineering

Data Quality & Governance

• Implement comprehensive data validation, testing, and monitoring frameworks to ensure accuracy and consistency across all energy and financial data assets
• Establish data lineage tracking and privacy controls to meet regulatory compliance requirements in the energy sector
• Develop alerting and monitoring systems for data pipelines, including error handling, SLA monitoring, and incident response

CI/CD & DevOps

• Lead continuous integration and deployment initiatives for Dagster and dbt pipelines, and Streamlit/Gradio application deployments to Linux servers
• Implement automated testing and deployment automation for data pipelines and analytics applications
• Manage version control and infrastructure as code practices

Cross-functional Collaboration

• Partner with Analytics Engineers, Data Scientists, and business stakeholders to understand requirements and deliver solutions
• Work closely with asset management and trading groups to ensure real-time data availability for market operations and risk calculations
• Collaborate with credit risk teams to develop data models supporting financial analysis and regulatory reporting
• Translate business requirements into technical solutions and communicate data insights to stakeholders

Documentation & Security

• Create and maintain technical documentation, data dictionaries, and onboarding materials for data assets
• Implement role-based access controls, data encryption, and security best practices across the data stack
• Monitor and optimize cloud infrastructure costs, implement resource allocation strategies, and provide cost forecasting

What You'll Bring

• Experience: 4+ years of hands-on data engineering experience in production environments
• Education: Bachelor's degree in Computer Science, Engineering, or a related field
• Data Orchestration: Proficiency in Dagster for pipeline scheduling, dependency management, and workflow automation; Airflow experience a plus
• Cloud Data Warehousing: Advanced-level Snowflake administration, including virtual warehouses, clustering, security, and cost optimization
• Data Transformation: Proficiency in dbt for data modeling, testing, documentation, and version control of analytical transformations
• Programming: Strong Python and SQL skills for data processing and automation
• CI/CD: 3+ years of experience with continuous integration and continuous deployment practices and tools; proficiency in GitLab CI/CD required (GitHub Actions experience a plus)
• Database Technologies: Advanced SQL skills, database design principles, and experience with multiple database platforms
• Cloud Platforms: Proficiency in AWS/Azure/GCP data services, storage solutions (S3, Azure Blob, GCS), and infrastructure as code
• Data Integration: Experience with APIs, various data connectors, and formats
• Data Security: Understanding of data security best practices, access controls, encryption, and role-based access management
• AI & LLM Proficiency: Practical experience integrating and leveraging large language models (e.g., OpenAI, Anthropic, or open-source models) within data workflows; ability to apply LLMs efficiently and securely, with awareness of data privacy boundaries, prompt injection risks, and responsible AI usage in production environments
• Problem-Solving: Strong analytical and troubleshooting skills with attention to detail
• Communication: Ability to work effectively with both technical and business stakeholders
• Work Style: Comfortable with a hybrid work environment (2-3 days in office); highly autonomous and self-directed with a strong work ethic and growth mindset; exceptional attention to detail; able to take ownership of projects end-to-end with minimal oversight

What Would Be Nice

• Experience in the energy, utilities, or financial services industries
• Knowledge of energy trading concepts, market data, or asset management
• Familiarity with data governance frameworks and regulatory compliance (SOX, GDPR, CCPA)
• Experience with infrastructure as code (Terraform, CloudFormation)

Clearway will not sponsor non-immigrant visas for this position (H-1B, TN, E-3, etc.).

#LI-Hybrid

What The Role Is

We are seeking a talented Senior Data Engineer to design, build, and maintain our data infrastructure supporting mission-critical energy operations. You'll work at the intersection of renewable energy and data, developing pipelines that process everything from real-time asset performance data to complex trading and risk analytics. This hybrid role offers the opportunity to make a direct impact on clean energy operations while working with a cutting-edge data stack including Snowflake, Dagster, dbt, Modal, and GitLab.

What You'll Be Doing

Analytics Infrastructure & Data Warehouse Management

• Design, deploy, and maintain scalable data infrastructure to support enterprise analytics and reporting needs
• Manage Snowflake instances, including performance tuning, security configuration, and capacity planning for growing data volumes
• Optimize query performance and resource utilization to control costs and improve processing speed

Data Pipeline Development & Orchestration

• Build and orchestrate complex ETL/ELT workflows using Dagster to ensure reliable, automated data processing for asset management and energy trading
• Develop robust data pipelines that handle high-volume, time-sensitive energy market data and asset generation and performance metrics
• Implement workflow automation and dependency management for critical business operations

Data Transformation & Analytics Support

• Develop and maintain dbt models to transform raw data into business-ready analytical datasets and dimensional models
• Create efficient SQL-based transformations for complex energy market calculations and asset performance metrics
• Support advanced analytics initiatives through proper data preparation and feature engineering

Data Quality & Governance

• Implement comprehensive data validation, testing, and monitoring frameworks to ensure accuracy and consistency across all energy and financial data assets
• Establish data lineage tracking and privacy controls to meet regulatory compliance requirements in the energy sector
• Develop alerting and monitoring systems for data pipelines, including error handling, SLA monitoring, and incident response

CI/CD & DevOps

• Lead continuous integration and deployment initiatives for Dagster and dbt pipelines, and Streamlit/Gradio application deployments to Linux servers
• Implement automated testing and deployment automation for data pipelines and analytics applications
• Manage version control and infrastructure as code practices

Cross-functional Collaboration

• Partner with Analytics Engineers, Data Scientists, and business stakeholders to understand requirements and deliver solutions
• Work closely with asset management and trading groups to ensure real-time data availability for market operations and risk calculations
• Collaborate with credit risk teams to develop data models supporting financial analysis and regulatory reporting
• Translate business requirements into technical solutions and communicate data insights to stakeholders

Documentation & Security

• Create and maintain technical documentation, data dictionaries, and onboarding materials for data assets
• Implement role-based access controls, data encryption, and security best practices across the data stack
• Monitor and optimize cloud infrastructure costs, implement resource allocation strategies, and provide cost forecasting

What You'll Bring

• Experience: 4+ years of hands-on data engineering experience in production environments
• Education: Bachelor's degree in Computer Science, Engineering, or a related field
• Data Orchestration: Proficiency in Dagster for pipeline scheduling, dependency management, and workflow automation; Airflow experience a plus
• Cloud Data Warehousing: Advanced-level Snowflake administration, including virtual warehouses, clustering, security, and cost optimization
• Data Transformation: Proficiency in dbt for data modeling, testing, documentation, and version control of analytical transformations
• Programming: Strong Python and SQL skills for data processing and automation
• CI/CD: 3+ years of experience with continuous integration and continuous deployment practices and tools; proficiency in GitLab CI/CD required (GitHub Actions experience a plus)
• Database Technologies: Advanced SQL skills, database design principles, and experience with multiple database platforms
• Cloud Platforms: Proficiency in AWS/Azure/GCP data services, storage solutions (S3, Azure Blob, GCS), and infrastructure as code
• Data Integration: Experience with APIs, various data connectors, and formats
• Data Security: Understanding of data security best practices, access controls, encryption, and role-based access management
• AI & LLM Proficiency: Practical experience integrating and leveraging large language models (e.g., OpenAI, Anthropic, or open-source models) within data workflows; ability to apply LLMs efficiently and securely, with awareness of data privacy boundaries, prompt injection risks, and responsible AI usage in production environments
• Problem-Solving: Strong analytical and troubleshooting skills with attention to detail
• Communication: Ability to work effectively with both technical and business stakeholders
• Work Style: Comfortable with a hybrid work environment (2-3 days in office); highly autonomous and self-directed with a strong work ethic and growth mindset; exceptional attention to detail; able to take ownership of projects end-to-end with minimal oversight

What Would Be Nice

• Experience in the energy, utilities, or financial services industries
• Knowledge of energy trading concepts, market data, or asset management
• Familiarity with data governance frameworks and regulatory compliance (SOX, GDPR, CCPA)
• Experience with infrastructure as code (Terraform, CloudFormation)

Clearway will not sponsor non-immigrant visas for this position (H-1B, TN, E-3, etc.).

#LI-Hybrid

What We're Looking For

We are looking for a Senior Application Security Engineer to help secure our AI, machine learning, and inference platforms. This is a hands on technical role focused on building security into modern AI infrastructure, inference systems, and developer platforms.

You will work closely with platform engineers, ML engineers, and infrastructure teams to identify risks, design secure architectures, and build security tooling that enables engineers to move quickly and safely.

This role is execution focused. You will drive technical implementation, perform deep security reviews, and help build out our application security capabilities alongside the CISO and engineering leadership.

This role can be based out of one of our office hubs (San Francisco or Seattle) with in-office requirements of at least 2 days per week, plus occasional team/company offsites. We are not able to offer visa sponsorship for this position at this time.

What You’ll Do

Secure AI and Machine Learning Systems

• Perform threat modeling across AI platforms, inference services, and ML pipelines
• Identify risks such as prompt injection, model extraction, adversarial inputs, and data leakage
• Review model serving architectures and inference pipelines
• Partner with ML engineers to secure training, fine tuning, and deployment workflows
• Help design isolation and security controls for multi tenant AI workloads

Application Security Engineering

• Perform architecture and design security reviews
• Conduct targeted code reviews for high risk components
• Identify security gaps in APIs, micro-services, and distributed systems
• Build secure patterns for authentication, authorization, and service to service communication
• Help engineering teams implement secure defaults and guardrails

Inference Platform Security

• Secure customer facing inference APIs and services
• Protect against abuse, model extraction, and adversarial behavior
• Design rate limiting, isolation, and workload protection controls
• Build monitoring and detection for anomalous inference behavior

AI Supply Chain and Model Security

• Evaluate open source models and dependencies
• Secure model artifacts and distribution pipelines
• Implement integrity validation and provenance controls
• Help secure container images and runtime environments

Security Automation and Tooling

• Build security automation for AI and application pipelines
• Integrate security scanning into CI/CD workflows
• Develop tooling to help engineers detect and fix issues early
• Improve developer experience with security guardrails

What You'll Need

Required Experience

• Strong background in application security engineering
• Experience performing threat modeling and architecture reviews
• Experience securing APIs and distributed systems
• Experience working in cloud environments such as AWS, GCP, or Azure
• Experience with containers and Kubernetes
• Strong scripting or programming skills such as Python, Go, or similar
• Experience working closely with engineering teams to implement security improvements

AI and Machine Learning Experience

• Experience securing ML pipelines, inference systems, or data platforms
• Familiarity with risks such as prompt injection, model extraction, and adversarial inputs
• Experience reviewing model serving architectures
• Understanding of training data security and data leakage risks

It's a Strong Plus If You Have

• Red team or offensive security experience
• Experience crafting payloads and evaluating CVEs for exploitability in diverse environments
• Experience with GPU infrastructure or high performance computing
• Experience with Hugging Face, PyTorch, TensorFlow, or similar frameworks
• Experience with LLM systems, RAG pipelines, or agent frameworks
• Experience building security automation pipelines
• Experience securing multi tenant infrastructure

What Success Looks Like

• Security is embedded into AI platform architecture early
• Engineering teams ship quickly with secure defaults
• Inference platforms are resilient against abuse and extraction
• Model pipelines are secure and auditable
• Security tooling scales with engineering growth

Why This Role Matters

This role sits at the intersection of application security, AI infrastructure, and developer platforms. You will help define how we secure modern AI systems while enabling engineers to move quickly and safely.

You will work closely with engineering leadership and the CISO to build practical, scalable security capabilities that support rapid innovation in AI and machine learning.

Compensation

The Role

We are seeking a Staff Machine Learning Engineer with deep expertise in agentic AI — and a true passion for experimentation and creation — to design, build, test, evaluate, and productionize next-generation autonomous AI agents for healthcare and clinical research. If you love rapidly prototyping wild ideas, running build-test-learn cycles, iterating on novel agent behaviors, and turning unsolved challenges into working systems, this is the role for you. You will own end-to-end agentic workflows that reason, plan, use tools, orchestrate multi-agent collaboration, and deliver safe, reliable outcomes in highly regulated environments, while collaborating with multidisciplinary teams to influence Medeloop’s technological direction. You will also be nested within a team of advisors and collaborators with deep medical and health expertise, including scientists, clinicians, and AI experts, including the former FDA commissioner, former editor of JAMA, and developer of BloombergGPT. The result: You will be an active participant in fostering a data lead public health and healthcare ecosystem. 

What You'll Own

• Lead the design and architecture of advanced agentic AI systems, including reasoning loops (ReAct, CoT, ToT), tool-calling, dynamic multi-agent orchestration, RAG pipelines, memory/state management, and emerging protocols like Model Context Protocol (MCP) and Agent-to-Agent (A2A).
• Build and own production-grade agent infrastructure, including prompts, function tools, workflow graphs, MCP/A2A integrations, and adaptive agent lifecycle management (spinning up, specializing, delegating, and decommissioning agents dynamically for complex healthcare workflows).
• Develop rigorous evaluation and safety frameworks — automated testing, benchmarking, regression testing, adversarial testing, safety guardrails, observability (tracing, logging, metrics), and human-in-the-loop mechanisms to ensure reliable, compliant performance in production.
• Drive LLM and ML model development — train, fine-tune, and deploy large-scale models on healthcare datasets, working closely with researchers and clinicians to solve real clinical challenges.
• Shape Medeloop’s agentic AI strategy and roadmap in close partnership with the C-suite and cross-functional leadership.
• Stay at the cutting edge of agentic AI (multi-modal agents, advanced reasoning models, interoperability protocols) and help establish Medeloop as a leader in transparent, compliant healthcare AI.

What We're Looking For

• 7+ years of hands-on experience as a Machine Learning Engineer, with a proven track record building and shipping production agentic AI systems (single- or multi-agent) in industry, ideally in healthcare, life sciences, or other related domains.
• Experience working on analytic engines (or advanced analytics platforms) — designing, optimizing, or integrating systems that power data-driven insights, queries, or decision-making at scale.
• Strong theoretical foundation in ML/AI, with emphasis on NLP/LLMs, reinforcement learning, planning/reasoning algorithms.
• Deep expertise with agentic frameworks and tools: LangChain/LangGraph, Model Context Protocol (MCP), Agent-to-Agent (A2A) protocols, Hugging Face, PyTorch, vector databases/semantic search, prompt engineering, and observability platforms (e.g., LangSmith, Phoenix).
• Experience designing fully automated evaluation and testing pipelines for autonomous agents and their orchestration, including metrics for reliability, safety, factuality, cost/latency, clinical utility, and dynamic behaviors.
• A builder/experimenter mindset — you thrive on rapid prototyping, testing bold new ideas, iterating quickly on agent designs, and exploring uncharted territory in agentic systems.
• Passion for unsolved challenges in healthcare AI, with the ability to thrive in a fast-paced, multidisciplinary environment and wear multiple hats.

Bonus Points

• Strong record in top AI/ML conferences/journals; experience with healthcare data (EHRs, claims) and regulatory considerations (HIPAA, transparency, reproducibility).
• Multi-cloud experience (AWS, Azure, GCP)

Why Medeloop

• Ownership from day one: small team, high-trust, no layers between your work and its impact
• Technically ambitious: you'll build AI-powered workflows, not just support them
• Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter
• Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

About You and The Role

Zipline builds and operates fleets of delivery drones to get medicine to those who need it, fast, regardless of where they live. To power this, the software team is building out the long term scalable solutions to expand rapidly while empowering our world class distribution centers to serve their customers as fast as possible.

Zipline’s security problems aren’t “website got pwned” problems (though those exist too). They’re “real-world autonomy + robotics + global operations + cloud software + regulated/health-adjacent workflows” problems. You’ll partner deeply with software, infrastructure, and (where relevant) embedded/autonomy teams to reduce real risk in real systems. We have a large attack surface 

Our ideal candidate works well in startup environments, wears many hats, and collaborates across engineering disciplines. You’ll join a small, high-ownership security team with significant influence over how we scale.

A note on our modern reality and agentic tooling: 

Engineering teams are increasingly adopting LLM copilots and agentic tools to move faster. That’s useful, until an “assistant” becomes an unmonitored automation path to secrets, sensitive data, or privileged actions. (Think: “obedient intern with production credentials.”) Industry guidance is converging on practical frameworks like the NIST AI Risk Management Framework (including a profile for generative AI) and the OWASP Top 10 for LLM Applications, which explicitly calls out risks like prompt injection, insecure plugin design, and excessive agency. 

In this role, you’ll help Zipline safely leverage these tools while containing them so they don’t quietly “rewrite the threat model”.

This is a Hybrid onsite role - you will frequently have conversations in person at our HQ in South San Francisco.

What You'll Do

• Own security outcomes for critical parts of Zipline’s application and cloud ecosystem (not by writing policy docs that no one reads, but by shipping controls and enabling teams).
• Partner with engineering teams on secure architecture, threat modeling, and design reviews for services that must be correct, reliable, and defensible under real-world operational pressure.
• Help us build and scale a pragmatic secure SDLC – CI/CD hardening, dependency/supply-chain controls,  secrets management, and code review patterns that don’t slow teams down.
• Improve cloud security posture end-to-end: IAM and least privilege, network/service-to-service trust, key management, logging/telemetry, runtime detection, and incident-ready auditability.
• Drive vulnerability management that actually closes risk: triage, exploitability analysis, remediation partnerships, and verification.
• Help build and exercise incident response: playbooks, tabletop exercises, logging requirements, and “know it happened / know what changed” operational discipline.
• Support data classification and access control models aligned to how Zipline operates (including partner/customer interfaces and global operations).
• Support external penetration tests and turn results into durable improvements, not whack‑a‑mole patches.
• Contribute to security compliance efforts (e.g., SOC 2 / ISO 27001) in a way that strengthens engineering
• Secure AI-assisted and agentic engineering workflows (this is explicitly part of the job):
• define safe patterns for copilots/LLM tools used in development and ops
• implement guardrails for sensitive data exposure and output handling
• prevent “agentic overreach” (over‑privileged tools, unsafe tool-calling, silent action-taking)
• build monitoring/auditing around AI tool use where it matters
  
  

What You'll Bring

• 8+ years of experience designing, building, and operating security controls for large-scale production systems (application, cloud, and infrastructure security).
• Strong security engineering chops with evidence you can reduce risk in production systems (not just talk about it).
• Hands-on ability to write and ship code/tools in Python, Go, or similar (you’re expected to build, not just review).
• Practical experience securing microservice architectures and modern cloud stacks (containers/Kubernetes, IAM, CI/CD, secrets, logging).
• Comfort operating as a technical leader without authority: you can persuade, teach, and unblock - not police.
• A skeptical mindset: you naturally ask “what’s the failure mode?” and “how will this be abused?” before shipping changes.
• Familiarity with the security failure modes of LLM-enabled systems (or the willingness to learn fast), including risks called out by OWASP such as prompt injection, insecure output handling, insecure plugin design, and excessive agency.

Nice To Haves

• Experience spanning multiple engineering domains (web app + cloud infra + embedded/robotics/autonomy).
• Experience building developer-friendly security platforms (internal libraries, paved roads, CI integrations, Public Key Infrastructure).
• Track record of being an effective security “evangelist” (i.e., enabling good behavior with good tools and defaults, not fear).
• Experience designing guardrails for internal AI/agent usage (policy + technical controls + auditing), especially in environments where safety and reliability are non-negotiable.
• Deep understanding of distributed systems and how failures actually happen (partial outages, weird retries, cascading dependencies, misconfigurations, permissions drift).

What Else to Know

This will be an in-office or hybrid role based out of our South San Francisco HQs. 

The starting cash range for this role is $230,000 - $275,000; please note that this is a target, starting cash range for a candidate who meets the minimum qualifications for this role. We are always open to negotiation. The final cash pay for this role will depend on a variety of factors, including a specific candidate's experience, qualifications, skills, working location, and projected impact. The total compensation package for this role may also include: equity compensation; overtime pay; discretionary annual or performance bonuses; sales incentives; benefits such as medical, dental and vision insurance; paid time off; and more.

Zipline is an equal opportunity employer and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws or our own sensibilities.

We value diversity at Zipline and welcome applications from those who are traditionally underrepresented in tech. If you like the sound of this position but are not sure if you are the perfect fit, please apply.

About the Team:

Our diverse Product & Engineering team values innovation, collaboration, and the continuous improvement of our platform. We offer a dynamic work environment where employees can grow and thrive while working on cutting-edge technology that shapes the future of AI and search.

At AlphaSense, we believe that diverse experiences and perspectives are critical to our success. We are committed to creating an inclusive workplace where all employees feel valued and empowered to be their authentic selves.

About the Role

AlphaSense is seeking an experienced engineering leader to transform how we build and operate AI-powered systems at scale. You'll join a team of brilliant engineers who've built cutting-edge AI applications, and your mission will be to bring world-class engineering practices that ensure these innovations run reliably for our enterprise customers.

This is a unique opportunity for a seasoned engineer who thrives on building robust platforms, mentoring talented teams, and establishing engineering excellence. You'll have significant autonomy to shape our technical architecture while working with frontier AI models and agentic systems that power market intelligence for the world's leading companies.

What You'll Do

• Architect for Scale: Design and implement distributed systems that power AI agents processing thousands of requests per hour, ensuring reliability, performance, and cost-efficiency
• Build Engineering Excellence: Establish comprehensive testing strategies, observability systems, and CI/CD pipelines that catch issues before customers do
• Lead Through Expertise: Mentor a team of smart, motivated engineers, sharing your experience in building production systems that don't break at 3 AM
• Drive Platform Evolution: Own the technical roadmap for our AI platform, making architectural decisions that will shape our systems for years to come
• Bridge AI and Engineering: Collaborate with ML engineers and researchers to productionize cutting-edge AI capabilities while maintaining system stability

What We're Looking For

Required:

• 7+ years building and operating distributed systems in production
• Track record of improving system reliability (taking services from frequent outages to 99.9%+ uptime)
• Deep expertise in modern engineering practices: microservices, containerization (Kubernetes), infrastructure as code
• Experience leading technical initiatives and mentoring engineering teams
• Strong coding skills with ability to work across the stack
• Excellence in debugging production issues and implementing comprehensive observability
• History of making pragmatic trade-offs between perfect and shipped

Preferred:

• Experience with LLM applications, agent frameworks, or AI/ML infrastructure
• Familiarity with prompt engineering, RAG patterns, vector databases
• Background at high-growth companies or modern engineering cultures
• Experience with multi-model AI systems (OpenAI, Anthropic, Google, etc.)

Impact You'll Have

Within your first year, you'll transform how we build and operate software. You'll reduce incidents, establish testing and observability standards that become part of our DNA, and build platforms that enable other engineers to ship faster and safer. Most importantly, you'll elevate an entire team of engineers, sharing the expertise that only comes from years of building systems at scale.

The Company: 

Faraday Future is a California-based technology company focused on the design, engineering, and development of intelligent, connected electric vehicles and related artificial intelligence–enabled technologies. 

Founded in 2014, the Company's mission is to disrupt the automotive and technology industries by creating user-centric, technology-first experiences. The Company, together with its controlled subsidiaries, operates across multiple technology-driven areas, including AI electric vehicles, robotics, and its crypto business (AIXC), all under its upgraded Global EAI Industry Bridge Strategy, marking the beginning of a new chapter in AI mobility and Web3 integration. The Company aims to leverage the latest technologies and world's best talent to realize exciting new possibilities across all of these lines. Faraday Future's automotive business exemplifies its vision for luxury, innovation, and performance, while its FX strategy aims to introduce mass production models equipped with state-of-the-art luxury technology derived from the FF brand, targeted towards a broader market with middle-to-low price range offerings. FF is committed to redefining mobility through AI innovation. Join us in shaping the future of intelligent transportation and technology by creating something new, something connected, and something with a true global impact. 

Your Role: 

The Senior Staff AI Engineer, Generative AI Platforms & Workflows will lead the engineering of AI-native platforms and automation systems that power content production, creative operations, and cross-functional workflows inside FF's Visual Marketing and broader creative organization. 

This role sits at the intersection of AI engineering, agent orchestration, and business workflow automation, with the goal of transforming how FF's marketing, content, and operations teams work — turning manual, time-consuming processes into scalable, AI-powered systems. You will design and ship AI agents, tool integrations, and production-grade automation that enable a small team to deliver with the output of a much larger one. 

This is a hands-on engineering role. You will architect and build the systems yourself — multi-agent orchestration, memory and knowledge layers, model routing, tool integration, and the user-facing interfaces that sit on top. You will also contribute to FF's broader EAI strategy by helping develop internal AI platforms that the Company plans to roll out across its marketing, creative, and operations functions. 

This position reports to the Head of Visual Marketing and works cross-functionally with marketing, content, product, and engineering teams to align AI-powered platforms with business objectives and measurable outcomes. 

This is a full-time on-site role based in Los Angeles, California, requiring regular in-office collaboration. Fluency in both English and Chinese is required. 

Key Responsibilities: 

• AI Workflow Engineering: Build AI-powered workflow systems that help marketing, content, and operations teams automate complex creative and business tasks — from asset generation and campaign operations to research, review, and distribution pipelines. 

• Agent Platform Development: Design and implement multi-agent orchestration infrastructure — state machines, supervisor-executor patterns, task scheduling, memory and knowledge systems, and failure recovery — so that non-technical teams can configure and run their own AI teams. 

• Generative Content Pipelines: Architect end-to-end generative AI pipelines spanning image, video, copy, and multimodal asset generation to support FF's global brand marketing, product launches, and visual storytelling initiatives. 

• Tooling & Integration Layer: Integrate AI tools, MCP-based plugins, and third-party services (OpenRouter, Composio, Claude Code, Cursor, Midjourney, Runway, ComfyUI, and similar) into production-grade workflows that can be safely operated by business users. 

• AI-Native Engineering Practices: Use AI coding tools (Claude Code, Cursor, and equivalents) as your primary development method. Establish engineering workflows, prompts, and patterns that maximize one-engineer throughput through AI-assisted development. 

• Business-Aligned Delivery: Work directly with marketing, content, product, and operations stakeholders to understand pain points, translate messy business requirements into clean technical solutions, and measure impact on time-to-ship, content quality, and operational cost. 

• Platform & SOP Thinking: Design for reuse and scale — build shared components, asset libraries, SOPs, and self-serve tooling so creative and business teams can operate AI systems independently, without engineering bottlenecks. 

• Innovation & Experimentation: Continuously evaluate emerging models, agent frameworks, and creative AI tools. Prototype rapidly, validate against real business workflows, and bring proven technology into production. 

Basic Qualifications: 

• Education: Bachelor's degree or above in Computer Science, Electrical Engineering, or a related technical discipline. 

• Experience: 7-10 years of software engineering experience, with meaningful hands-on work in AI/ML systems, AI agents, or AI-enabled tools and products. 

• AI Engineering: Demonstrated ability to design and ship AI-powered products — not just calling APIs, but building orchestration logic, prompt systems, memory layers, and tool-integration chains. 

• LLM Fundamentals: Solid understanding of LLM capabilities and boundaries — context windows, prompting patterns (System Prompt, CoT, Few-shot), model selection, and cost/quality trade-offs. 

• AI-Assisted Development: Daily, proficient user of Claude Code, Cursor, or equivalent AI coding tools. Able to clearly demonstrate how these tools multiply your engineering output. 

• Business Sense: Comfortable working alongside non-technical teams (marketing, content, product, operations). Strong ability to translate ambiguous business needs into clean engineering solutions and to prioritize based on real-world impact. 

• Delivery Track Record: Has independently shipped at least one complete product, internal platform, or meaningful open-source contribution from idea to production — evidence of end-to-end delivery capability. 

• Fluency in both English and Chinese due to internal support for Global Team 
• Ability to work on-site in Los Angeles, CA. 

Preferred Qualifications: 

• Experience building multi-agent systems, AI workflow automation platforms, or creative AI production pipelines. 

• Familiarity with MCP protocol, Composio, Vercel AI SDK, LangGraph, CrewAI, LobeHub, or similar agent frameworks and tool-integration platforms. 

• Hands-on experience with generative media tools such as Midjourney, Flux, Runway, Pika, Sora, ComfyUI, or Unreal Engine / Blender real-time pipelines. 

• Full-stack development experience: TypeScript/Node.js, Next.js, or Tauri desktop applications. 

• Prior experience in marketing tech, creative production, media/entertainment, or other content-heavy industries. 

• Founding-engineer, solo-builder, or early-stage startup background — comfortable owning a product from zero to launch. 

• Strong product intuition — able to make UX decisions, prioritize features, and think from the user's perspective without a detailed PRD. 
• Interest in automotive, robotics, Web3, or autonomous systems, aligned with FF's broader EAI Industry Bridge Strategy. 

Salary Range:

($180,000-$200,000 DOE), plus benefits and incentive plans

Perks + Benefits: 

• Healthcare + dental + vision benefits (Free for you/discounted for family) 

• 401(k) options 

• Casual dress code + relaxed work environment 

Faraday Future is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. 

We're looking for an experienced Machine Learning Engineer to build and improve the models and ML systems that drive our protein design efforts. In this role, you'll deploy and optimize large-scale generative models for protein design, and develop the surrounding infrastructure and tooling that enable our ML and protein design scientists to work faster and more confidently. As an early member of a small, fast-moving engineering team, you'll have significant ownership over our ML stack and the opportunity to shape how our platform evolves.

Responsibilities

• Build robust, reproducible and user-friendly pipelines for automated model fine-tuning, alignment and evaluation 
• Design and implement  modular, easy-to-maintain, multi-model pipelines for protein design.
• Develop highly scalable ETL pipelines to process petabyte-scale protein data for model pretraining
• Optimize model training and inference code to maximize throughput and resource utilization when deployed at scale
• Develop software and infrastructure that enable the ML team to work quickly and frictionlessly in distributed and multi-cloud environments
• Partner with ML and protein design scientists to prototype research ideas and bring them into production

Who You Are

• You're comfortable taking ownership and working independently in a fast-moving environment
• You're an execution-oriented engineer who maintains high standards, and focuses on the highest-impact work
• You're comfortable owning the full stack of your work, from training code to the infrastructure it runs on
• You care deeply about model quality, efficiency, and reliability
• You're willing to step beyond your core responsibilities when the team needs it

Representative Projects

• Building hyperparameter search frameworks for SFT and Alignment workflows
• Increasing protein language model throughput during long context generation
• Updating existing model architectures to work and run efficiently on new GPU hardware
• Implementing a protein design pipeline that integrates prompt retrieval, sequence generation, attribute prediction, and structure prediction
• Establishing an ETL pipeline for sampling and tokenizing training datasets from an internal database of billions of sequences
• Developing a benchmarking and evaluation system for newly trained sequence generation models
• Contributing to the development of an internal service that provides transparent multi-node job submission for ML scientists

Qualifications

• BS or MS in Computer Science, Machine Learning, or a related field
• 3+ years of hands-on experience building and training ML models in PyTorch
• Strong Python and software engineering fundamentals, including testing, code quality, and version control
• Experience profiling, benchmarking, and optimizing ML model training and inference
• Experience implementing or optimizing transformer-based architectures
• Familiarity with cloud infrastructure and containerization (GCP, AWS, Azure, Kubernetes, Docker)
• Strong fundamentals in ML, statistics, and/or linear algebra

Preferences (but not required)

• Familiarity with protein language models or computational biology
• Experience with GPU-level optimization (CUDA, Triton)
• Experience with distributed training (DDP, FSDP, multi-node GPU clusters)
• Experience with databases and data processing pipelines
• Experience orchestrating multi-step ML workflows
• Experience building backend systems that serve ML models in production
• Contributions to open source ML projects or published research

What We Offer

• High-growth opportunity with meaningful impact on the future of protein design
• Competitive compensation package with equity participation
• 401(k) with a strong employer match
• Comprehensive benefits including health/dental/vision insurance
• Generous PTO policy and commitment to work-life balance
• Professional development opportunities in a cutting-edge field at the intersection of AI and biology

Profluent Bio, Inc is an equal opportunity employer promoting diversity and inclusion in the workspace. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical conditions, veteran status, sexual orientation, gender (including gender identity and gender expression), sex (which includes pregnancy, childbirth, and breastfeeding), genetic information, taking or requesting statutorily protected leave, or any other basis protected by law.

Employment Eligibility Verification

Legal authorization to work in the United States is required. In compliance with federal law, all persons hired must verify their identity and work eligibility and complete the required employment verification form upon hire.

The Streaming & Media Engineering Team at Major League Baseball is seeking a Senior Software Engineer to join our Los Angeles, CA office. In this role, you will help build the next generation of platforms powering world-class fan experiences across MLB’s streaming products, including live and on-demand video processing, live content insertion, and custom orchestration software that integrates with cutting-edge broadcast equipment and operations. As a key contributor, you will design, build, and operate highly reliable distributed systems within MLB’s streaming ecosystem, while writing hands-on code, influencing technical direction, and playing an active role in design and code reviews. You will also collaborate closely with cross-functional partners across engineering, product, and operations.

Responsibilities

• Design, implement, and maintain scalable, cloud-native services that power live and VOD streaming workflows
• Decompose complex systems into well-defined services, APIs, and modules
• Write clean, efficient, well-tested, and maintainable code
• Participate in architecture/design reviews and provide thoughtful technical feedback
• Actively participate in Agile ceremonies (planning, stand-ups, retros, postmortems)
• Contribute to operational excellence through monitoring, alerting, and on-call support
• Mentor early-career engineers and promote engineering best practices
• Stay current with emerging technologies and industry trends

Qualifications & Skills

• Bachelor’s degree in Computer Science or related field, or equivalent practical experience
• 5+ years of professional software development experience building and supporting production systems
• Strong problem-solving skills and a track record of delivering high-quality solutions
• Experience with modern software development practices: Agile, source control, CI/CD, testing, and code reviews
• Strong experience with Core Java (or Kotlin) and Spring/Spring Boot
• Experience with messaging systems and relational/non-relational databases
• Experience with JavaScript (React and/or Node.js)
• Hands-on experience with cloud platforms (GCP and/or AWS)
• Experience with cloud-native technologies: Kubernetes, Docker, Helm, Terraform
• Experience building distributed, event-driven, non-blocking systems (e.g., async I/O, virtual threads, reactive patterns)

Bonus / Nice-to-Have Skills (AI / ML)

• Experience with Generative AI / ML APIs (e.g., LLMs, embeddings, speech-to-text, video/audio analysis) and common AI-powered workflows
• Understanding of prompt engineering and model evaluation, with interest in applying AI/ML to media and automation
• Exposure to live and/or VOD streaming media systems, including familiarity with HLS, FFmpeg, and SCTE standards.
• Experience with Google Ad Manager (GAM) and ad decisioning / ad insertion workflows

Salary Range: $150,000 - $185,000 (Base Salary + Bonus)

As a candidate for this position, your salary and related aspects of compensation will be contingent upon your work experience, education, skills, and any other factors MLB considers relevant to the hiring decision. In addition to your salary, MLB believes in providing a competitive compensation and benefits package for its employees.

Top MLB Perks & Benefits

• Competitive Benefits Package
• Company 401K Contribution
• Paid Time Off and Holidays
• Paid Parental Leave
• Access to Free Tickets to Baseball Games & MLB.TV
• Discounts at MLB Store | MLBShop.com
• Employee Assistance Programs (EAP)
• Onsite/Online Training & Development Programs
• Tuition Reimbursement
• Disability Benefits (short term and long term)
• Life and Accidental Death Insurance
• Pet Insurance

Role Description

As a Senior Value Engineer specializing in the Public Sector, you are pushing the envelope in solving critical operational challenges for State and Local Government agencies. You will be working with our most strategic public sector clients, understanding their unique objectives—from enhancing citizen services and modernizing legacy systems to optimizing grant management and regulatory compliance—and building Celonis solutions using the world’s leading Process Intelligence (PI) platform in combination with top AI and ML technology partners, such as Microsoft, OpenAI, and Databricks.

With Celonis’ Process Intelligence (PI) platform, we feed operational context to AI so it understands the complex realities of government operations (breaking down agency silos) and enables agencies to industrialize AI. This unlocks real ROI on AI deployments, maximizing taxpayer value at scale. There is no AI without PI. You will prototype these solutions, demonstrate their value to government CIOs and Agency Directors, and ensure successful implementation, adoption, and value realization to increase the footprint of Celonis across state and local governments.

Key Responsibilities:

• AI Discovery & Solutioning: Understand public sector AI strategies and specific agency challenges (e.g., benefits administration backlogs, procurement bottlenecks, public safety resource allocation, or permitting delays). As a Celonis product and government domain expert, find the best problem-solution fit and translate agency requirements into innovative solutions that deliver measurable public impact.

• Pre- and Post-Sales Execution: Actively drive the full customer lifecycle within the public sector. Lead technical discovery and capability demonstrations during the complex government pre-sales and procurement (RFP) cycles, and remain deeply involved post-sale to guide implementation, ensuring agreed value and adoption thresholds are successfully met.

• Hackathons & Prototyping: Think out of the box, have a „can-do“ attitude, and don’t shy away from complex legacy processes. Leverage cutting-edge AI technologies to rapidly build creative prototypes in agency hackathons, solving critical pain points to improve constituent experiences.

• Agentic Process Transformation: Support our government customers in achieving real value out of AI deployments at scale, enabling a fundamental shift from traditional, rigid, paper-heavy workflows to the use of autonomous AI agents empowered by our Celonis Process Intelligence Platform (e.g., intelligent case triaging or automated compliance checks).

• Proof Projects: End-to-end execution of critical Proof-of-Value projects. This includes architecting and delivering secure, scalable LLM/agent systems with RAG, tools, and guardrails, while seamlessly integrating with government enterprise data, identity protocols, and stringent compliance/security frameworks (e.g., StateRAMP, HIPAA, CJIS).

• Domain & Industry Leadership: Serve as the internal and external technical subject matter expert for the State & Local Government vertical, scaling knowledge across the organization regarding agency processes and public sector nuances.

Requirements:

• 5+ years of experience leading technical pre-sales and post-sales engagements specifically within the Public Sector (State & Local Government). This includes navigating government procurement cycles, building compelling ROI business cases for public funds, and guiding technical implementations through to constituent value realization.

• Deep understanding of business processes native to state and local governments (such as Health & Human Services, Procurement, Finance, DMV operations, or Public Safety) with the ability to translate high-level policy or agency needs into specific, impactful AI use cases.

• Expertise in generative AI techniques like RAG, few-shot learning, prompt engineering, multi-agent orchestration, multimodal understanding, or fine-tuning used to build high-impact use cases (e.g., intelligent constituent-facing chatbots, automated processing of policy documents, or grant application analysis).

• Solid knowledge of Python and common ML libraries (such as LangChain, pandas, pydantic, sklearn, PyTorch) as well as data engineering tools and technologies.

• Strong presentation skills to both internal and external stakeholders (including elected officials, agency directors, and IT leadership), whether leading technical whiteboarding sessions or formal readouts and demos.

• Bachelor’s Degree required; Master's Degree in computer science, engineering, mathematics, or related fields, or equivalent work experience preferred.

Nice to have (big plus):

• Hands-on experience building agentic systems using LLM orchestration, RAG, function calling, and prompt engineering, while ensuring safety through rigorous evaluations and guardrails suited for highly scrutinized public sector environments.

• Experience in deploying and monitoring models at scale within government-compliant cloud environments (e.g., AWS GovCloud, Azure Government).

• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, or other OSS packages.
  
  

Visa sponsorship is not offered for this role.

Learn More about Value Engineering at Celonis Here

About the Role

Fivetran needs an engineer-architect who can make the AI analyst experience real, dependable, and extensible. This role owns the architecture behind the analyst AI system, including metadata contracts, tool interfaces, context assembly, prompt and skill orchestration, evaluation loops, and the operational patterns that make agents useful in real workflows instead of isolated demos.

This is a systems role as much as an AI role. The challenge is not just getting an LLM to do something impressive once. The challenge is building a product-quality analyst system that can reason over schema and business context, use tools correctly, recover from ambiguity, and improve through disciplined evaluation and iteration.

This is a full-time, hybrid position based at our Oakland, CA office. Our hybrid work model offers a blend of remote flexibility and in-person collaboration, including two days in the office each week to connect and build as a team.

What You'll Do

• Define the architecture for the analyst AI stack across prompts, tools, metadata, memory and context strategies, evaluation, and runtime safety.
• Build the abstractions and interfaces that allow product teams to add new agent capabilities without creating brittle prompt sprawl.
• Design retrieval and context strategies that give the model the right information at the right time with strong cost, latency, and quality tradeoffs.
• Establish evaluation loops, benchmark suites, and review processes so model behavior can be measured, debugged, and improved systematically.
• Partner with product, engineering, and analyst-domain experts to translate real analytical workflows into robust AI-assisted product capabilities.
• Drive quality standards around correctness, trust, transparency, and failure handling in analyst-facing AI experiences.

What We're Looking For

• Deep experience building AI-enabled product systems, not just running experiments.
• Strong software architecture skills, especially around APIs, contracts, orchestration, and system boundaries.
• Practical understanding of LLM prompting, tool use, retrieval, structured outputs, and evaluation methods.
• Ability to work fluently across backend engineering, product constraints, and user workflow design.
• Clear judgment about where agent systems should be flexible and where they should be constrained.
• Comfort working in ambiguous environments where patterns are still being invented.

Why This Role Matters

The quality of the AI analyst experience will shape how differentiated Fivetran feels. This role determines whether the system is merely novel or actually reliable enough to become part of an analyst's day-to-day workflow. Strong architecture here reduces product risk and creates leverage for the rest of the team.

#LI-HYBRID #LI-DJ1

Who we are

Metropolis is an artificial intelligence company that uses computer vision technology to enable frictionless, checkout-free experiences in the real world. Today, we are reimagining parking to enable millions of consumers to just "drive in and drive out." We envision a future where people transact in the real world with a speed, ease, and convenience that is unparalleled, even online. Tomorrow, we will power checkout-free experiences anywhere you go to make the everyday experiences of living, working, and playing remarkable - giving us back our most valuable asset, time.

Who you are

Metropolis is seeking a Staff Software Engineer to join our Revenue Platform team. In this role, you will own the technical direction for our billing, payments, and invoicing infrastructure, charting the course for a system that already processes tens of millions in monthly transactions. You will balance immediate business needs with long-term architectural improvements, identifying high-impact enhancements and designing practical migration strategies to support our rapid growth.

As a technical leader, you will partner closely with engineering, finance, and operations teams to solve complex challenges involving pricing rules, subscription billing, and enterprise invoicing. You are a systems thinker who enjoys understanding how things work and building the roadmaps that improve a platform while it runs. Your work will directly shape how Metropolis monetizes its platform and expands into new verticals beyond parking, ensuring our financial systems remain resilient, compliant, and remarkably efficient.

What you’ll do

• Own the technical architecture and evolution of Metropolis's billing, payments, and invoicing systems, establishing standards for financial accuracy, transaction integrity, and compliance that ensure every dollar is correctly captured and accounted for
• Design and implement resilient payment processing with support for multiple payment gateways (Stripe,  etc.), intelligent routing between processors, retry strategies for failed transactions, and graceful handling of partial failures to maximize payment success rates
• Build comprehensive monitoring and reporting for revenue operations, including payment success rates, billing accuracy metrics, reconciliation dashboards, failed transaction alerts, and audit trails that provide visibility from customer transactions to accounting close
• Partner with finance and operations teams on payment failures, billing disputes, refund workflows, and reconciliation issues – building tools and automation that reduce manual intervention and improve mean time to resolution for customer-impacting revenue issues
• Drive adoption of revenue system best practices across services, including idempotent payment processing, distributed transaction handling, compensation workflows for failures, audit logging, and testing strategies that ensure financial correctness
• Own integrations with external systems, including payment gateways, ERP, data analytics, etc., ensuring reliable data flow and maintaining compliance requirements across the revenue stack

What we’re looking for

• 8+ years of hands-on engineering experience with a focus on building revenue systems like payment platforms
• Strong systems thinking for financial correctness with proven ability to design distributed revenue systems that handle edge cases gracefully: partial failures, duplicate requests, timing issues, currency conversion, tax calculation, and maintaining transaction integrity across service boundaries
• Database and financial data expertise, including transaction isolation, audit logging, point-in-time recovery, handling financial data migrations, and experience with systems that require both high availability and absolute correctness
• Payment gateway and integration experience with production knowledge of Stripe, PayPal, or similar processors; tax calculation services; accounting system integrations; and the operational reality of depending on external financial services, including compliance requirements like PCI-DSS
• Excellent technical communication with the ability to influence technical decisions across teams (particularly finance, accounting, and data teams), document complex systems, conduct post-mortems, and establish reliability standards organization-wide
• Experience with sophisticated usage of rapidly developing AI-assisted development skills in real-world codebases, including fluent use of Claude Code/GPT Codex/Windsurf or similar tools in multi-agent mode, spec-driven development (SDD),  MCP/skills optimization, context/prompt engineering
• Local to the Seattle area

While not required, these are a plus

• BS or MS in Computer Science or a relevant engineering discipline
• Expert-level revenue systems engineering with hands-on experience building billing platforms, payment processing pipelines, reconciliation systems, and handling challenges like idempotent transactions, failed payment retries, proration logic, multi-currency support, and revenue recognition – you've seen these problems before and know the patterns that work
• Expert revenue systems engineering with hands-on experience building billing platforms, payment processing, reconciliation, and managing complex challenges like idempotent transactions, payment retries, proration, multi-currency support, and revenue recognition.
• Financial systems expertise with deep experience implementing audit trails, reconciliation workflows, revenue reporting, and compliance controls at scale – you understand what it takes to maintain penny-perfect accuracy
• Java expertise with a deep understanding of JVM performance, concurrency, and operational characteristics. Scala experience is a big plus
• Experience with AI-powered development tools such as Claude Code, GitHub Copilot, or similar agentic coding tools for enhanced productivity

  Our Stack

• Languages + Frameworks: TypeScript, React, Scala (principally), Java (limited)
• Datastores: MySQL, PostgreSQL, Snowflake
• Cloud: AWS
• Version control: Git & GitHub
• AI Tooling: Copilot on GitHub and Claude Code
• Observability: Datadog

4 Days in Office: Metropolis values in-person collaboration to drive innovation, strengthen culture, and enhance the Member experience. Our corporate team members hold to our office-first model, which requires employees to be on-site at least four days a week, fostering organic interactions that spark creativity and connection.

When you join Metropolis, you'll join a team of world-class product leaders and engineers, building an ecosystem of technologies at the intersection of parking, mobility, and real estate. Our goal is to build an inclusive culture where everyone has a voice and the best idea wins. You will play a key role in building and maintaining this culture as our organization grows. The anticipated base salary for this position is $220,000 USD to $250,000 USD annually. The actual base pay offered is determined by several variables, including, as appropriate, the applicant's qualifications for the position, years of relevant experience, distinctive skills, level of education attained, certifications or other professional licenses held, and the location of residence and/or place of employment. Base pay is one component of Metropolis's total compensation package, which may also include access to or eligibility for healthcare benefits, a 401(k) plan, short-term and long-term disability coverage, basic life insurance, a lucrative stock option plan, bonus plans, and more.

Company: interface.ai Location: Palo Alto, CA (In-Office) Reports to: VP of Product Compensation: $250,000 - $320,000 base salary + bonus + equity (depending on location & experience)

About interface.ai

Banking hasn't kept up with how people actually live their financial lives. At interface.ai, we're building AI-native products that transform how millions of Americans interact with their financial institutions, making every interaction intelligent, personal, and proactive.Our platform powers AI agents that handle real banking tasks end-to-end: moving money, resolving issues, converting loans, and surfacing financial insights across voice, chat, and digital channels. We're not building another chatbot. We're building the AI infrastructure that will become the primary way people bank, unlocking a massive market opportunity by displacing the legacy systems that financial institutions have been stuck with for decades.

With two-thirds of Americans living paycheck to paycheck, only 17% financially literate, and 50% holding less than $500 in savings, the urgency is real. We're putting financial well-being on autopilot.

We power AI agents for 100+ financial institutions, serving millions of banking members across the U.S., and are backed by leading investors including Avataar Venture Partners and Blume Ventures.

Our platform enables banks and credit unions to automate high-volume customer interactions across voice and digital channels while improving resolution rates, reducing operational costs, and increasing customer engagement.

Key credibility anchors:

• Series B AI company building agentic AI infrastructure for financial institutions
• Trusted by 100+ banks and credit unions across North America
  
• Serving millions of end users through conversational AI across voice, chat, and mobile channels
  
  

The Opportunity

You will own the AI platform that every product at the company depends on. This is not a support function: the platform is the competitive moat. Your decisions directly determine the ceiling of what every other product team can build.

You'll inherit a multi component platform, assess what exists, and define what comes next: what to consolidate, what to invest in, what to sunset. As the senior IC in a pillar of 3-4PMs, you'll set technical direction, mentor, and raise the quality bar, while partnering with engineering leadership to scale the system to millions of end users at startup speed.

What You'll Own

• Agent runtime & orchestration: multi-agent orchestration, memory/context management, tool routing, goal planning, and policy-aligned behavior
• Platform contracts: APIs, versioning, deprecation, and migration strategy: you define the boundaries and the lifecycle for all consuming teams
• Model lifecycle: selection, evaluation, benchmarking, fine-tuning enablement, and prompt optimization. You define when to prompt-tune vs fine-tune vs switch models.
• Inference performance & reliability: latency budgets, caching, batching, quantization, multi-region deployments, fallbacks, and safe rollout patterns (canaries, feature flags, rollback)
• Safety & compliance: PII detection/masking, prompt/response safety policies, tenant isolation, SOC2/GDPR readiness, audit logs, and encryption: compliance is a product requirement, not an afterthought
• Evaluation & quality: offline golden sets, regression testing, hallucination measurement, task success metrics, and production-to-improvement feedback loops
• Voice & multimodal: ASR/TTS integration patterns, WER, interruption handling, turn-taking, and latency budgets for real-time conversational intelligence
• Pillar leadership: You set technical direction, mentor, raise the bar, and have input on hiring. You prioritize competing demands from multiple product pillars, define the platform investment thesis, and protect the platform from becoming a feature factory.

Who You Are

You think in systems, not features. You read a platform outage postmortem and immediately see the product decision that caused it. You've built API contracts that entire product orgs depend on, and you've made the hard call to break one when the platform needed to evolve.

You understand the full AI stack: from model training through inference optimization to production monitoring. You can spec a fine-tuning pipeline, set latency budgets for real-time voice, and debate quantization trade-offs with a staff engineer, all in the same week.

You build your own tools. You use AI to prototype, analyze, and accelerate your work — building agents, writing scripts, creating automated workflows, whatever gets the job done fastest. Not because someone told you to, but because you can't imagine working any other way.

What You Bring

• 8+ years in product management, with 3+ years at Staff/Principal level on platform, AI/ML, or infrastructure products
• Owned a multi-component platform serving multiple product teams, not just a single product
• Deep technical fluency: can read code, review architecture, and debug production issues alongside engineers
• Experience with AI/ML platforms: LLM orchestration, model evaluation, fine-tuning, RAG, agentic frameworks, or similar
• Experience governing platform contracts and managing how internal teams consume capabilities
• Built in enterprise SaaS environments with multi-tenant, compliance, and governance requirements
• AI-native workflow: You build with AI daily — creating agents, automating analyses, prototyping with code, building custom workflows. The specific tools don't matter (Cursor, Copilot, Claude, Replit, custom scripts — all count); what matters is that you've moved beyond prompting into building.

Bonus Points

• Financial services or regulated industry experience
• Voice/ASR/TTS systems and real-time latency-sensitive products
• LLM evaluation frameworks, multi-agent orchestration, or RLHF/RLAIF
• Knowledge graph or RAG-based retrieval systems
• Built a platform team from scratch

Our Values

• Act Like You're the Founder: Own the outcome from start to finish
• No Fear. Speak Your Mind: Candor moves us forward
• Pursuit of Excellence: Raise the bar, every time
• AI-First Mindset: Think automation, intelligence, and scale-first in every solution
• Don't Assume. Seek to Understand: Start with questions, not conclusions

How We Work

Every PM builds with AI daily. Our standard toolkit is Cursor, MCP integrations, and custom AI agents — and we onboard every new hire on these tools in their first week. We hold a high quality bar and move at startup speed. Flat organization. No empire-building.

What We Offer

• 100% company-paid medical, dental, and vision coverage
• 401(k) with company match and financial wellness perks
• Discretionary PTO + paid parental leave
• Mental health, wellness, and family benefits
• Competitive equity with meaningful upside

We are seeking a Senior Software Engineer to contribute to the development of next-generation products as a key team member driving impactful features from concept to production. You will collaborate across engineering, product, and design to translate business requirements into clear, scalable technical solutions, leveraging modern methodologies—including AI where it aligns with product scope—to deliver high-quality products and efficient software workflows.

You are someone who thrives in ambiguous problem spaces, has strong product instincts, and can translate customer needs into technical solutions that drive business outcomes.

Who You Are:

• 5+ years of professional experience with a focus on high-growth product environments
• Strong coding and architectural proficiency—you prioritize moving quickly down the right path to ship reliable code daily
• Full-stack proficiency—comfortable across backend services, APIs, and modern frontends (React, Angular, or Vue.js)
• Strong product instincts—you translate customer needs into technical solutions that drive business outcomes
• AI-First mindset—expertly using AI coding tools to accelerate delivery without compromising architectural integrity
• Agile execution—you understand the lifecycle of a feature from local branch to production monitoring

Nice to Have

• Fluency in our core stack (Django, Python, React, and React Native)—accelerates time-to-impact and onboarding
• Experience with advanced deployment patterns—leveraging feature flags, config-driven behavior, and progressive rollouts
• Exposure to LLM-based systems—includes prompt engineering, tool-call architectures, or agent workflows
• Domain experience in two-sided marketplace or recruitment platforms

What You'll Do:

• Product Delivery – Collaborate with PMs and designers to translate customer needs into technical solutions and shape product requirements.
• Write Quality Code – Ship code that follows engineering best practices (DRY, maintainable) and is production-safe after code review.
• Collaborate Cross-Functionally – Work closely with Product, Design, and Data partners to ensure project alignment from concept through deployment.
• Drive Personal Growth – Proactively seek opportunities to sharpen skills through code reviews and architectural deep-dives with senior engineers.
• Own Features End-to-End – Drive feature development from coding through deployment, monitoring, QA, and iteration based on user feedback.
• Advocate for Best Practices – Champion engineering excellence and contribute to continuous improvement in developer tooling and experience.
• Contribute Technical Input – Provide input on team technical design, balancing short-term delivery with long-term scalable solutions.
• Practice Clear Communication – Communicate effectively through clear PR descriptions and proactively surfacing blockers before they become critical problems.

For CA-based Applicants:

• The base salary for this position is $170,000 - $200,000.
• This position is eligible for equity in the form of stock options
• This position is eligible for Instawork benefits, including:
• A variety of medical, dental, and vision plans with coverage beginning on the date of hire
• Flexible paid time off
• At least 8 paid company holidays annually
• Phone stipend
• Commuter stipend
• Supplemental pay on qualified leaves
• Employee health savings accounts (HSA) contribution
• Flexible spending plans
• 401K plan
• Perkspot - discount program through Lumity

A variety of factors are considered when determining someone’s compensation including a candidate’s professional background, experience, and location.  Final offer amounts may vary from the amounts listed above.

About the Job

At BuildOps, we’re building a groundbreaking software solution designed to empower today’s commercial contractors. From service management to project execution, we’re reimagining how our customers operate. Our team thrives on ambition, innovation, and collaboration—qualities we look for in every new hire.

As our Founding Engineer, Agentic Business Transformation, you will embed directly within business functions across the organization, deeply understand how teams operate today, and fundamentally reimagine how they should operate tomorrow. This is not about bolting automation onto existing processes; it's about redesigning how work gets done with agentic tooling transforming (and eliminating) workflows. You will partner with leaders across GTM and G&A to surface high-impact opportunities, architect intelligent solutions, and deliver measurable gains in productivity, output quality, and the experiences we deliver to our customers and employees. You will define what "AI-native operations" looks like inside a high-growth B2B SaaS company—well before most companies have even started asking the question. This is a high visibility role and will involve daily contact with the CEO, Chief of Staff, Chief Revenue Officer, and other members of the BuildOps Executive Leadership Team (ELT). 

ROLES AND RESPONSIBILITIES 

• Embed within internal business functions, including Revenue, RevOps, Customer Success, Customer Support, Finance, People, and Talent, to develop a deep, firsthand understanding of current workflows, pain points, and decision-making processes 
• Conduct structured discovery, shadowing teams, interviewing stakeholders, and mapping end-to-end processes, to identify where AI can eliminate, redesign, or dramatically accelerate work 
• Reimagine workflows from first principles rather than automating existing steps; challenge assumptions about what requires human involvement and what doesn't 
• Design, build, and deploy AI-powered automations, agents, and internal tools that reduce time spent on repetitive, low-value-add tasks while improving the quality and consistency of outputs 
• Integrate large language models, agentic frameworks, and automation platforms into production-grade internal workflows with appropriate guardrails, error handling, and human-in-the-loop checkpoints 
• Measure everything: establish baselines before intervention, track adoption and usage patterns post-deployment, and quantify productivity impact in terms of hours saved, error rates reduced, and cycle times compressed 
• Serve as a trusted advisor to functional leaders on what AI can and cannot do for their teams; translate business problems into technical solutions and technical constraints into business language 
• Stay abreast of the rapidly evolving AI tooling landscape (e.g., LLM capabilities, orchestration frameworks, RPA-to-AI convergence, no-code/low-code platforms) and calibrate our internal stack accordingly 
• Build reusable patterns, templates, and playbooks so that successful automations in one function can be adapted and scaled across others 
• Collaborate closely with the engineering and product organizations to ensure internal automation efforts align with our broader technology strategy and do not create shadow IT risk 

QUALIFICATIONS 

• 5+ years of professional experience building automations, intelligent workflows, or AI-powered solutions in a business context—whether that foundation was in robotic process automation, software engineering, solutions engineering, or applied AI 
• Demonstrated ability to work directly with non-technical business teams: you have spent meaningful time understanding how people actually work before designing what to build 
• Hands-on experience with modern AI tooling, including large language models (e.g., Claude, GPT), prompt engineering, and at least one orchestration or agentic framework (e.g., LangChain, CrewAI, n8n, or similar) 
• Strong scripting and integration skills with the ability to connect APIs, build data pipelines, and stand up end-to-end workflows in production—not just prototypes
• Track record of automating processes that previously required human judgment—not just rules-based tasks, but work involving unstructured data, natural language inputs, or contextual decision-making
• Process-oriented mindset: you naturally decompose complex workflows into discrete steps, identify bottlenecks, and think in terms of inputs, outputs, and handoffs
• Comfort operating with high autonomy in a fast-moving environment; you don't wait to be told what to build — you go find the highest-leverage problem and propose a solution
• Excellent communication skills: you can present a business case to the CEO, C-suite executives, or VPs, run a discovery session with an ops team, and write clear documentation for a workflow you've built
• Experience in B2B SaaS, vertical software, or a similarly complex operational environment is a strong plus
• Familiarity with process mining, workflow analytics, or structured methodologies for identifying automation candidates (e.g., lean, six sigma, or consulting-style process improvement) is a plus

COMPENSATION

• [$188,000 to $273,000 base] + bonus opportunity (LA or SF Hub)

As the leading transit bus manufacturer in the United States, GILLIG buses play a critical role in the environmental and social initiatives in communities across our nation.  GILLIG is on the forefront in the transition to zero-emission vehicles and has an unmatched reputation in the industry for quality, reliability, durability, service, and support.  From initial design through final assembly, each GILLIG bus is designed and built in Livermore, CA. 

WHY GILLIG

• Leader in environmentally friendly mobility solutions including battery electric, hybrid electric, near-zero CNG and clean diesel transit buses
• Renowned for its inclusive team/family-oriented culture
• Stable, successful, and growing organization – a Bay Area business for over 132 years!
• Flexible schedules (*depending on project needs)
• Excellent compensation including company paid medical premiums, generous retirement plan and other comprehensive benefits

DEPARTMENT OVERVIEW

GILLIG Engineering is responsible for the development, customization, and configuration of GILLIG’s clean technology heavy-duty transit buses.  We are committed to developing the most reliable buses that connect communities, eliminates congestion, and contributes to a greener environment.  Our engineers are engaged in designing, analyzing, testing, launching, and problem solving across a wide range of technical areas.

Liaison Engineering serves as the critical link between design and manufacturing teams, ensuring smooth and efficient production of GILLIG’s high-quality transit buses. Their core mission is to bridge the gap between engineering design and the manufacturing floor in Livermore, CA. They support current products, facilitate seamless integration of new designs and ensure continuous improvement of production processes. The Liaison Engineering Group is a cross-functional and multi-talented group, housing mechanical, electrical, vehicle controls, and industrial engineering as well as dedicated production support technicians to ensure coverage in all facets of producing transit buses.

ABOUT THE ROLE

As an Engineer in Liaison Engineering, you will take on a pivotal role, providing technical support to engineers and technicians in tasks related to the assembly of transit buses. You will have significant influence at the component level to optimize parts and systems. You will collaborate closely with a diverse team of engineers, technicians and other professionals, fostering a collaborative environment to achieve common goals. Your role will involve leading projects, mentoring junior team members, and ensuring the successful implementation of engineering solutions and improvements through effective communication and coordination.

This is an on-site position at our Livermore, CA plant that will report directly to the Lead Mechanical Engineer of Production Liaison but may also receive direction and guidance from senior engineers.

IN THIS ROLE YOU WILL

• Demonstrate a strong understanding of engineering design principles and manufacturing processes, improving project outcomes through accurate and manufacturable designs
• Proficiently troubleshoot and resolve production issues to ensure consistent flow and high-quality outputs, maintaining smooth operations with minimal downtime, and provide prompt technical support during production
• Perform root cause analysis, resolve complex engineering challenges, design processes, and support manufacturing of heavy-duty transit buses
• Apply experience with lean manufacturing principles and process optimization, achieving better resource utilization and cost efficiency
• Utilize knowledge of quality assurance standards and practices, ensuring compliance and high-quality production
• Facilitate cross-functional collaboration, achieving project milestones through improved communication
• Evaluate and address rapid requests from the supply chain, ensuring timely response to supply chain needs
• Ensure manufacturability of designs, enhancing design feasibility and production efficiency
• Develop and improve production processes, ensuring efficient and reliable manufacturing
• Optimize manufacturing processes to improve efficiency, enhancing overall production performance
• Act as a key contributor in providing Design for Manufacturability (DFM) and Design for Assembly (DFA) input to design teams
• Prepare and maintain process documentation to support safety standards, compliance with safety regulations and ensure a safe work environment to reduce assembly risks

SCOPE OF THE ROLE 

• Autonomy: Ability to work with little instruction on day-to-day work, receiving general instruction on new assignments from senior engineers or manager.
• Technically Minded: Apply industry practices and standards (NFPA, NEC, APTA, etc.). Exhibit professional expertise, applying design standards, best practices, and company procedures to resolve a variety of issues. Determine a course of action based on guidelines and modify processes and methods as required.
• Time Management: Work productively to deliver projects on schedule and on budget.  Solve production and/or manufacturing related issues expeditiously. 
• Communication: Collaborate up, down and across the organization to resolve mutual problems. Focus on providing standard professional advice and creating initial reports/analysis for review by experienced team members.  
• Complexity: Responsible for a certain set of commodities or design elements within the team. Work on problems of moderate to more-complex scope where analysis of situations or data requires a review of a variety of factors. Exercises judgment within defined procedures and practices to determine appropriate action. Applies learned techniques and contributes to analysis and investigation to solve problems.  
• Quality & Standards: Technical documentation is accurate and complete. Released designs are cost effective, manufacturable, serviceable, meet all performance objectives, and customer requirements.

BASIC QUALIFICATIONS

• Bachelor or advanced degree in Engineering 
• Minimum 2 years of experience within the Engineering industry 
• Proficient mechanical troubleshooting and understanding of quality standards
• Experience with “hands-on” troubleshooting, product assembly, prototyping, testing, and validation activities
• Understanding of manufacturing processes lean manufacturing principles
• Fluency in CAD software
• Familiarity with BOM structures 
• Ability to create and understand mechanical drawings
• Experience in the designing of components including Design for Manufacturing (DFM), Design for Service (DFS), and cost-efficiency (Value Engineering) 
• Experience working with component suppliers, engineering service suppliers, testing suppliers and product validation engineers 
• Automotive, commercial and/or heavy-duty vehicle design familiarity, direct experience is a plus
• Effective collaboration with cross-functional teams

WORK ENVIRONMENT

• Ability to lift 25 lbs.
• Prolonged periods of sitting/standing at a desk and working on a computer
• Regularly required to sit, stand, and walk and occasionally kneel or squat
• The ideal candidate must be able to complete all physical requirements of the job with or without a reasonable accommodation.
• Limited travel may be required
• Must be able to navigate manufacturing environment, comfortable around heavy machinery, tools, etc.
• Must be able to wear Personal Protective Equipment, (i.e. safety glasses, bump caps, hearing protection, etc.)
• Standard start time is 6:00AM (negotiable)

EXPECTED COMPENSATION

$75,000 - $130,000/annual salary + premium benefits  

Pay offered may vary depending on multiple individualized factors, including market location, job classification, job-related knowledge, skills, and experience. The total compensation package for this position may also include other elements dependent on the position offered. Details of participation in these benefit plans will be provided if a candidate receives an offer of employment.

Other Duties

Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. Duties, responsibilities and activities may change at any time with or without notice.

GILLIG is committed to providing equal opportunities to all employees and applicants for employment. We are committed to creating an inclusive and diverse workplace that values and respects the unique talents, experiences, and perspectives of our employees and the people we serve. As an equal opportunity employer, we do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, genetic information, sexual orientation, pregnancy, gender identity or any other characteristic protected by law.

GILLIG is an equal employment opportunity and affirmative action employer.

The Role:

CommerceIQ is pioneering a new frontier in retail tech by building the Retailer Copilot with Agents. This is a next-generation AI platform to empower retail teams with intelligent, autonomous workflows.

We’re seeking a curious, hands-on, and customer-obsessed Director of Product Management with an AI focus to join our incubation team. You’ll be at the intersection of cutting-edge LLM technologies and real-world retail execution problems, helping conceptualize, prototype, test, and scale agent-based solutions across our customer base. A note that this is an individual contributor role.

If you thrive in zero-to-one environments, love working at the bleeding edge of AI, and enjoy sitting down with customers to dig into their workflows and co-create the future, this role is for you.

Why Join Us

• Be part of CommerceIQ’s newest frontier, shaping the AI agent ecosystem for retail.
  
  
• Work directly with visionary leaders and founding engineers.
  
  
• Build products with real impact that are tested and deployed with Fortune 500 brands.
  
  
• Get full exposure to AI, product, customer, and GTM strategy.

Location / Travel:

• This is an onsite role based out of our Mountain View, CA headquarters. You will be expected to work onsite 5 days per week if hired.

 What You'll Do:

AI-Native Product Thinking

• Identify and validate customer pain points that are ripe for agent-based automation.
  
  
• Own the AI-first product strategy: leverage LLMs, fine-tuning, RAG, and tool use to craft intelligent workflows.
  
  
• Design and evaluate product concepts with agentic behavior, personalization, and embedded intelligence.
  
  

Rapid Prototyping & Testing

• Create and test AI-driven prototypes, either hands-on or with the support of forward-deployed engineers.
  
  
• Drive quick iteration cycles using real customer feedback, eval metrics, and qualitative insights.
  
  
• Build a repeatable framework to test and prioritize use cases and agent behaviors.
  
  

Customer Discovery & Co-Creation

• Lead deep-dive discovery sessions with senior retail & e-commerce stakeholders.
  
  
• Translate ambiguous customer workflows into structured agent-enabled solutions.
  
  
• Evangelize concepts early and build conviction with pilot partners.
  
  

Use Case Library & Playbooks

• Develop a structured, living library of agent use cases across functions and retailers.
  
  
• Create internal playbooks and blueprints for agent design, AI evaluation, and success criteria.
  
  

Cross-Functional Execution

• Collaborate closely with Applied AI, Forward Deployed Engineering, Design, and GTM teams.
  
  
• Operate as a mini-CEO of your use cases, from exploration to launch and customer adoption

What You'll Bring:

• 6-12 years of experience in product management or equivalent roles (AI prototyping, ML PM, solutions engineer with PM mindset).
  
  
• Demonstrated interest in AI: LLMs, RAG, evals, prompt engineering, fine-tuning, etc.
  
  
• Hands-on experience with prototyping tools or notebooks (e.g., LangChain, Streamlit, Python, etc.). By no means do you have to be an engineer, but you must have a technical mindset and associated aptitude.
  
  
• Strong product intuition and ability to simplify complex systems into compelling UX.
  
  
• Experience leading customer conversations, running discovery interviews, and testing early concepts.
  
  
• Comfortable with ambiguity, high ownership, and fast-paced 0 to 1 environments.
  
  
• Willingness to travel up to 40% for customer immersion and pilots.
• Bachelor's degree, preferably in Business, Computer Science, or a related field

Nice to Haves:

• Background in retail, e-commerce, or SaaS for enterprise workflows.
  
  
• Experience building agent-based systems, copilots, or AI assistants.
  
  
• Prior startup or incubation team experience.

Compensation and Benefits: 

The typical base pay range for this role in Mountain View, CA is: $203,000 - $229,000/per year. This role also qualifies for an annual bonus and initial equity package in the form of stock options.

The indicated base pay range may be inclusive of multiple career levels at CommerceIQ and will be narrowed based on a number of factors including the candidate’s experience, qualifications, and location.  

Base salary, bonus, and equity are just one part of your total rewards package at CommerceIQ. You will also receive access to:

• Comprehensive medical, vision, and dental coverage
• A 401(k)-retirement plan
• Short & long-term disability insurance 
• Life insurance
• Paid parental leave
• Monthly reimbursements for gym, phone, and internet
• 10+ paid company holidays in each calendar year, quarterly Global Recharge Fridays, and unlimited PTO

Check out our LinkedIn page to learn more about what it’s like to work at CommerceIQ!

P-1607

At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.

As one of the first engineers in the NYC Engineering office, you’ll have the opportunity to be part of a small, nimble team that’s innovating to build new products from the ground up. Our goal is to leverage the power of Databricks in data & AI to deliver vertical AI applications for both technical and business users. This will be a true 0-to-1 environment, combining the excitement of a startup with the resources of a tech leader like Databricks.   

The impact you'll have:

• Create novel, never-seen-before interfaces for GenAI agents that manage complex workflows while keeping the human in the loop through inspectability and transparency
• Partner closely with product management, design, and other engineering teams to build intuitive, scalable, and extensible solutions that drive user & business growth
• Lead by example with hands-on full-stack software development to create dynamic, user-centric experiences
• Enable mechanisms that can drive product-led growth, through seamless onboarding and sharing capabilities
• Direct the future of intuitive agentic applications at Databricks, in collaboration with cross-functional teams of innovative software engineers, product managers, and designers
• Define and drive the strategy for building end-to-end AI feature development by designing, building, and maintaining systems that are scalable, reliable, and performant.

What we look for:

• 10+ years of experience with HTML, CSS, and JavaScript
• Demonstrated product mindset with the ability to translate ambiguous customer problems into scrappy MVPs and iterate quickly based on data and user feedback.
• You care that AI features feel intuitive, trustworthy, and helpful, not just bolted on to an existing app
• High ownership and bias for action in 0→1 environments: you are comfortable making pragmatic trade-offs, operating with incomplete information, and driving projects from idea through launch and adoption.
• Strong ability to collaborate across product, engineering, and design teams to align technical strategy with company growth objectives.  
• Experience with modern JavaScript frameworks (e.g., React, Angular, or VueJs/Ember)
• 10+ years of experience with server-side web technologies (eg: Node.js, Java, Python, Scala, C#, C++,Go)
• Proven experience building and shipping end-to-end generative AI products, ideally with a significant UI/UX component
• Proven experience building and shipping end-to-end generative AI products
• Deep familiarity with LLMs and generative AI, including hands-on experience with techniques like retrieval-augmented generation (RAG), prompt design, evaluation, and monitoring AI quality and safety in production.

Why You’ll Love This Role

We are seeking a talented and experienced Staff AI Platform Engineer to help build Cribl’s new Corporate AI Systems team. In this pivotal role, you will design, deploy, and operate the governed AI platform that enables secure, scalable AI across Cribl’s internal systems and workflows. This is a foundational role on a newly established team created to provide the shared infrastructure, security guardrails, and reusable patterns needed to turn AI from fragmented experimentation into a durable company capability.

You will be instrumental in standing up the shared AI infrastructure layer that powers high-impact use cases across GTM, Prod-Eng and G&A functions. The team’s mandate is to provide the “paved road” for AI at Cribl: secure access, governed integrations, reusable workflows, and a platform that enables teams to move faster without creating security, compliance, or operational risk.

This role will report to the Sr. Director, Enterprise Applications and will partner closely with stakeholders across Enterprise Applications Engineering, Security, IT, and the business teams adopting AI to build the core platform that every subsequent AI initiative runs on.

As An Active Member Of Our Team, You Will…

As the Staff AI Platform Engineer, you will be the foundational builder of Cribl’s shared corporate AI platform. Your key responsibilities will include:

• AI Platform Architecture & Operations: Define and own the architecture for Cribl’s internal AI platform, LLM deployments, MCP gateway design, orchestration patterns, and the shared services required to run AI use cases safely at scale.
• Secure Access, Identity & Token Governance: Establish the identity and access model for AI systems, including distinct non-human identities, scoped credentials, audit logging, cost controls, and token governance infrastructure that supports least-privilege access.
• Sandboxed Enablement & the Paved Road: Build safe, reusable sandbox environments and self-service patterns that allow business and technical teams to experiment with AI inside a governed framework rather than through ad hoc or unapproved tooling.
• Enterprise Integration Architecture: Design the connective tissue between AI tooling and Cribl’s enterprise systems, helping define secure patterns for integrating with platforms such as Salesforce, NetSuite, Workday, Jira, Confluence, Slack, Google Drive, Glean, and other business-critical tools.
• AI Security Partnership: Work hand in hand with the AI Security team to ensure secrets management, MCP governance, prompt-injection defenses, AI telemetry, and compliance-ready controls are built into the platform from day one rather than bolted on later.
• Engineering Enablement: Stand up the platform capabilities needed for AI-accelerated development, including AI coding infrastructure and guardrails, DevOps pipeline integration, and secure workflows that help builders move faster without compromising quality or security.
• Platform Reliability, Adoption & Operating Effectiveness: Define and track the metrics that matter most for a shared AI platform, including platform availability, reliability, usage, adoption, guardrail effectiveness, cost efficiency, and time to enable new use cases. Partner with adjacent stakeholders to support measurement of the  business impact as the platform scales across the company.
• We are a remote-first company and work happens across many time-zones – you may be required to occasionally perform duties outside your standard working hours 

If You’ve Got It - We Want It

• Staff-level platform engineering experience: 7+ years of experience in software engineering, platform engineering, infrastructure engineering, internal developer platforms, or closely related technical roles, with a track record of building shared capabilities used by many teams.
• AI platform fluency: Strong hands-on experience with modern LLM and agentic systems, including enterprise AI platforms, API-driven model integration, retrieval patterns, and the practical realities of getting AI safely into production.
• Identity, security, and governance depth: Proven experience with OAuth, service identities, RBAC / ABAC / scoped permissions, auditability, secrets management, and secure-by-default architecture patterns.
• Integration architecture expertise: Experience designing and operating integrations across enterprise systems, APIs, workflow platforms, and event-driven architectures in complex SaaS environments.
• Practical systems mindset: Ability to balance speed, reliability, usability, and governance. You know how to build a platform that enables teams rather than slows them down.
• Cross-functional communication: Strong written and verbal communication skills, with the ability to simplify complex technical tradeoffs for business leaders, security partners, and technical peers alike.
• Builder mentality: You are comfortable creating the first version of the operating model, the runbooks, the patterns, and the platform itself. Ambiguity energizes you.
• Outcome orientation: You care about measurable business impact, not just elegant architecture. You understand that the platform only matters if it helps Cribl ship useful, governed AI capability
• Preferred qualifications:
  • Experience with AWS Bedrock, Claude Code, or similar enterprise AI platforms.
  • Experience with MCP, skills, gateway technologies, API mediation, tool-use architectures for AI agents.
  • Familiarity with orchestration and workflow platforms such as n8n, Workato, and adjacent automation frameworks.
  • Experience deploying guardrails for AI-assisted engineering, including approval flows, policy enforcement, observability, and secure coding controls.
  • Familiarity with enterprise systems such as Salesforce, NetSuite, Workday, Jira, Confluence, Slack, Google Drive, and Glean.
  • Experience operating in a high-growth, remote-first B2B SaaS environment.
  • Comfort partnering closely with Security, IT, GTM Ops, Finance, People, and Support stakeholders.
  • Good jokes, or maybe better, bad jokes.
  • A love for goats.

Salary Range ($169,000 - $250,000)

The salary for this role is dependent on geographic location. The salary offered within the range described will be based on the individual candidate’s job-related knowledge, skills, and experience.  In addition to a competitive salary, Cribl also offers a generous benefits package which includes health, dental, vision, short-term disability, and life insurance, paid holidays and paid time off, a fertility treatment benefit, 401(k), equity, and eligibility for a discretionary company-wide bonus.

#LI-KJ1
#LI-Remote

We're looking for a Senior SDET who thinks deeply about quality in systems that are inherently non-deterministic. Agentic AI doesn't fail the same way traditional software does — and testing it requires a new toolkit: eval frameworks, prompt regression, tool-call reliability, adversarial scenarios, and more.

You'll own the entire quality infrastructure across our product portfolio — from test data and CI pipelines to the standards and culture of how we ship. You'll work directly with product, devops, and AI engineering, with no layers between your decisions and their impact.

What You'll Own

• Test infrastructure, test data, test processes across the entire product portfolio while working with Devops and Infrastructure engineers
• Test Framework - build and enhance automated testing frameworks and tools that facilitate automated testing across different layers of application.
• The reliability bar for all Web applications, Mobile applications and AI agent outputs — from hallucination detection to latency regressions and tool-call correctness
• Test infrastructure, test data, and test processes across the entire product portfolio, alongside DevOps and Infrastructure engineers
• Automated testing frameworks that span all layers of the application — unit, integration, contract, and end-to-end
• Evaluation frameworks designed for LLM-based systems: non-deterministic output scoring, prompt regression, and adversarial test suites
• HIPAA-aware test data management — de-identification pipelines, synthetic data generation, and audit trail validation
• Integration of automated tests into CI/CD pipelines for continuous delivery confidence
• Build stability monitoring and release gate enforcement before any deployment
• Documentation of test plans, test results, and evaluation standards to support knowledge sharing
• The "safety net" for product quality — you define what done looks like

What We're Looking For

• 8+ years of hands-on SDET experience, with recent work building or testing agentic AI systems (single- or multi-agent) in production
• Experience in healthcare or life sciences — you understand what's at stake when a system fails in this domain
• A true tester's mindset: you seek out edge cases, adversarial inputs, and failure modes others overlook
• Proficiency across the full test pyramid — unit, integration, system, performance, and exploratory — plus familiarity with LLM-specific evaluation approaches
• Strong debugging skills across multi-tier web and mobile architectures; comfortable jumping into production incidents
• Proficiency with testing frameworks such as Jest, React Testing Library, Supertest, and pytest.
• Hands-on experience with testing tools like Cypress, Playwright, Supertest, and pytest (including requests or Selenium-based testing)
• Experience testing RESTful APIs using tools like Postman or Supertest
• Solid command of JavaScript and Python

Bonus Points

• Multi-cloud experience (AWS, Azure, GCP)
• Experience with red-teaming or adversarial testing of AI systems
• Native mobile testing experience (iOS, Android)
• Prior work with 21 CFR Part 11, GxP, or similar regulated-software validation frameworks

Why Medeloop

• Ownership from day one: small team, high-trust, no layers between your work and its impact
• Technically ambitious: you'll build AI-powered workflows, not just support them
• Real-world stakes: your work accelerates drug development, addresses health equity, and improves clinical research for institutions that matter

Strong foundation: Series A, top-tier investors, and a data asset (200M+ patient records) that most companies spend years trying to build

Why You’ll Love This Role

​We are seeking a talented and experienced Staff AI Security Engineer to help build Cribl’s new AI Systems team. In this pivotal role, you will design, implement, and operationalize security and governance frameworks that enable rapid AI adoption to scale safely across Cribl’s internal systems and workflows. This is a foundational role on a newly established team tasked with providing the shared infrastructure, security guardrails, and reusable patterns needed to turn AI from fragmented experimentation into durable company capabilities.

You will be instrumental in bringing security, governance, and safety to Cribl’s rapidly expanding AI footprint, including API tokens, secrets management, MCP security, shadow AI mitigation, AI telemetry, and compliance readiness. The team’s mandate is to provide the “paved road” for AI at Cribl: secure access, governed integrations, reusable workflows, and a platform that enables teams to move faster without creating security, compliance, or operational risk.

This role will be part of the Corporate AI Systems team and will report directly to the Chief Information Security Officer (CISO). It will partner closely with stakeholders across Security, Enterprise Applications, Product, Engineering, IT, Legal, and the various business teams adopting AI to ensure Cribl’s AI capabilities scale securely and pragmatically.

As An Active Member Of Our Team, You Will...

As the Staff AI Security Engineer, you will be the foundational builder of Cribl’s AI security and governance layer. Your key responsibilities will include:

• AI Security Architecture & Governance: Define, threat model, and operationalize the security architecture for Cribl’s internal AI platform, including standards, controls, approval patterns, and secure-by-design guidance for AI use cases before they scale into production.
• Shadow AI Discovery & Remediation: Partner with Business Operations to maintain visibility into AI tools, licenses, API tokens, MCP servers, and ad hoc workflows in use across the company, and monitor for ungoverned or high-risk patterns that require remediation.
• MCP Security & Registry Management: Own the framework for vetting MCP servers, maintaining an approved registry, defining risk tiers, and enforcing secure connection patterns as MCP adoption expands across teams.
• Secrets, Identity & Token Protection: Establish secure patterns for secrets management, non-human identities, scoped credentials, OAuth-based access, and token governance to enforce least-privilege access and reduce credential exposure in AI builds.
• Prompt Injection Defense & Safe Execution Controls: Design and deploy guardrails for prompt injection defense, deterministic validation, human-in-the-loop approvals, and additional controls for high-risk workflows that combine sensitive data, untrusted content, and external action.
• AI Telemetry, Detection & Incident Response: Partner on building Cribl as the observability backbone for AI systems, including telemetry pipelines, abuse detection, audit trails, threat hunting, and incident response patterns for AI-specific security events.
• Compliance & Customer Governance Readiness: Partner with Cribl’s Compliance team to drive documentation and control readiness for AI-related obligations and customer scrutiny, including NIST AI RMF, ISO 42001, EU AI Act readiness, AI acceptable use standards, and customer-facing AI governance materials.
• Secure AI-Assisted Corporate Engineering Enablement: Establish the security controls required for AI-assisted internal development, secure coding practices, secrets management, SCA/SAST/DAST expectations, and review patterns for AI-generated code and workflows.
• Risk Metrics & Security Effectiveness: Define and track the metrics that matter most for AI security, including shadow AI exposure, control coverage, incident trends, security review turnaround, and reduction of high-risk patterns as the platform scales across the company.
• We are a remote-first company and work happens across many time-zones - you may be required to occasionally perform duties outside your standard working hours.

If You’ve Got It - We Want It

• Staff-level security engineering experience: 7+ years of experience in security engineering, application security, cloud security, identity and access management, detection engineering, or related technical security roles, with a track record of building practical controls that scale.
• AI security fluency: Strong hands-on experience with modern LLM and agentic systems, including threat models for prompt injection, tool use, model access, RAG, AI coding tools, and API-driven integrations.
• Identity, secrets, and governance depth: Proven experience with OAuth, service identities, secrets management, RBAC / ABAC / scoped permissions, auditability, and secure-by-default architecture patterns.
• Security architecture judgment: Experience designing risk-tiered controls, approval models, and protective guardrails that balance innovation with real-world compliance and operational needs.
• Detection and incident response mindset: Ability to operationalize telemetry, define actionable detections, investigate security signals, and build pragmatic response paths for new threat surfaces.
• Compliance and customer trust orientation: Familiarity with frameworks and customer expectations relevant to enterprise AI governance, including NIST AI RMF, ISO 42001, SOC 2, GDPR, SOX, or adjacent control environments.
• Cross-functional communication: Strong written and verbal communication skills, with the ability to simplify risk, controls, and tradeoffs for engineers, business stakeholders, and senior leaders alike.
• Builder mentality: You are comfortable creating the first version of the registry, the standards, the playbooks, and the guardrails. Ambiguity energizes you.
• Outcome orientation: You care about materially reducing risk while enabling useful AI adoption. You understand that security only works if it is practical enough to be used.
• Preferred Qualifications
  • Experience with AI development tools like Claude Code, AWS Bedrock, or similar enterprise AI platforms.
  • Experience with MCP, skills, API security, gateway technologies, or tool-use architectures for AI agents.
  • Familiarity with multi-agent workflow design, workflow security patterns, and human-in-the-loop orchestration controls.
  • Experience with SCA / SAST / DAST, secrets management, SIEM / telemetry pipelines, and secure software delivery controls.
  • Familiarity with enterprise systems such as Salesforce, NetSuite, Workday, Jira, Confluence, Slack, Google Drive, and Glean, especially where AI workflows introduce differentiated risk.
  • Experience operating in a high-growth, remote-first B2B SaaS environment.
  • Comfort partnering closely with Security, IT, GTM Ops, Finance, People, Legal, and Support stakeholders.
  • Good jokes, or maybe better, bad jokes.
  • A love for goats.

Salary Range ($128,000 - $200,000)

The salary for this role is dependent on geographic location. The salary offered within the range described will be based on the individual candidate’s job-related knowledge, skills, and experience.  In addition to a competitive salary, Cribl also offers a generous benefits package which includes health, dental, vision, short-term disability, and life insurance, paid holidays and paid time off, a fertility treatment benefit, 401(k), equity, and eligibility for a discretionary company-wide bonus.

#LI-KJ1
#LI-Remote

Why You'll Love This Role

You will work closely with the founding team and a group of highly-skilled engineers to shape the future of search and analytics of observability data. You will play a central role in bringing integrating cutting-edge Generative AI technologies with the Cribl Product suite to help solve real customer problems.  You will work closely with development partners and key stakeholders to iteratively design, develop, and deliver products and surfaces that will delight our customers.
On top of it all you will have fun. Cribl strives to be a great place to work for everyone. 

As An Active Member Of Our Team, You Will...

• Work with the product engineering teams to prototype and experiment with new AI features
• Work across backend, frontend, and interact with LLMs and/or other ML models
• Productionize, launch, and operate AI-based technology integrations into Cribl’s core products with the goal of solving real customer problems
• Stay up-to-date with the latest AI technologies and trends
• Work closely with fine tuning models, Prompt Engineering, and Inference Platforms
• This position will require stand-by, on-call, or off-hours duties

If You've Got It - We Want It

• Bachelor's degree in CS/EE with 6+ years of industry experience
• Ability to problem solve from first principles and maintain focus on overarching objectives
• Prior experience in contributing to products that have gained significant user traction or scaled to a large user base
• Experience as an AI/ML Engineer
• Experience with ML Ops
• Experience with Typescript/JavaScript and either Node or React

Salary Range ($225,000 - $265,000)

The salary for this role is dependent on geographic location. The salary offered within the range described will be based on the individual candidate’s job-related knowledge, skills, and experience.  In addition to a competitive salary, Cribl also offers a generous benefits package which includes health, dental, vision, short-term disability, and life insurance, paid holidays and paid time off, a fertility treatment benefit, 401(k), equity, and eligibility for a discretionary company-wide bonus.

#LI-JB1

The Role:

As a Forward Deployed Engineer, you will partner closely with our founders and the AI Product Managers to prototype and deploy AI‑driven solutions that solve our customers’ most pressing problems. This is a founding engineering role with high visibility; you will report directly to our Head of AI Strategy and Outcomes. You will also partner with other members of the executive leadership team who are based at our Mountain View HQ, collaborate with our Bangalore‑based engineering team, and engage daily with enterprise customers. You will help define how we work with our most important customers, shape the company’s first ever forward deployed engineering function, and contribute to our mission of helping brands win in AI‑driven e-commerce.

Location / Travel:

This position is on-site in Mountain View, CA with approximately 50% travel expected to client locations.

What You'll Do:

• Build prototypes with customers: Work alongside our AI Product Manager to understand real‑world problems, rapidly scope use cases and create working prototypes. You’ll engage in hands-on discovery and convert insights into functional demos for prospective and existing customers.
• Integrate data and create new AI agents: Ingest data from disparate sources (ERPs, ecommerce platforms, custom APIs) and build agents that solve the specific use cases uncovered during discovery. This includes turning edge‑case business rules into runtime‑editable settings rather than hard‑coded logic.
• Own AI outcomes: Lead customer‑facing demonstrations and proof‑of‑concepts. You’ll instrument and tune models using production feedback loops to ensure prototypes deliver measurable outcomes.
• Rapidly iterate & generalize: Collaborate with engineering teams in Bangalore to transform prototypes into robust features. Your work will be implemented into our core product - patterns that succeed in one deployment (custom adapters, monitoring dashboards) should be packaged as reusable modules.
• Own the full lifecycle: Support engagements from pre‑sales scoping through post‑deployment refinement. You’ll help to negotiate scope, push back on unreasonable asks, and ensure long‑term customer success. Additionally, you’ll act as the technical liaison between customers, product managers, and engineering.
• Cultivate customer relationships: Develop deep empathy for our customers’ business drivers and serve as their advocate. Use clear, engaging communication to convey technical concepts to non‑technical stakeholders, build trust and handle high‑stakes conversations.

What You'll Bring: 

• Experienced engineer (4-6 years or more) with a strong foundation in software development (coding, problem solving and debugging).
  • Comfortable coding in Python or Java and learning new technologies quickly.
• Full‑stack & data integration skills. Experience connecting to APIs, working with SQL/NoSQL databases, streaming/ETL pipelines and deploying AI/LLM‑based agents.
• Customer‑facing problem solver. You enjoy working directly with external users, conducting discovery, and translating ambiguous requirements into working solutions. You have excellent communication skills and the ability to simplify complex technical topics.
• Rapid prototyper. You are biased towards action and are comfortable delivering minimal‑viable solutions, collecting feedback and iterating quickly.
• Collaborative & global. You’re effective working across time zones with leadership in California and engineering in Bangalore. You embrace CommerceIQ’s leadership principles of ownership, deep diving into details, getting stuff done, thinking from first principles and winning as a team.
• Independent & adaptable. You thrive in ambiguity, can manage your own backlog and priorities, and are motivated by the opportunity to create a new function.
• Bachelor's degree, in Computer Science, Mathematics, or a related field