As a principal engineer on the Online Systems team, you’ll join a team that powers Pinterest’s most business-critical online systems at massive scale, driving the reliability, efficiency, and evolution behind every core Pinner and Advertiser experience. You'll lead major efforts like multi-region deployment and Kubernetes migration, set the standard for operational excellence, and define the long-term vision for our online serving infrastructure, supporting machine learning and product innovation across the company. This is an opportunity for high-impact technical leadership, broad visibility, and cross-functional influence at the heart of Pinterest’s platform.

What you’ll do: 

• Improve reliability, scalability and infra efficiency for Pinterest’s critical online systems across storage and caching, online service and realtime analytics systems to support continued business growth.
• Lead cross-functional strategic initiatives to modernize Pinterest’s online serving stack through multi-region deployment, Kubernetes migration, and the adoption of cutting-edge technologies and industry best practices.
• Quality champion for online systems and the broad engineering organization, holding a high bar on execution quality and operational excellence.
• Drive the online systems vision and strategy for the next 3 years and beyond, and translate that strategy into cross-functional roadmaps with measurable outcomes.
• Foster a diverse and inclusive engineering culture that makes all feel welcome, and invest in mentorship, critical thinking, and candid feedback to strengthen individual and organizational growth.
  
  

What we’re looking for:

• 12+ years of software engineering experience with deep expertise in distributed systems, especially online serving, storage, and caching systems: hands-on experience building and operating highly available, reliable, production-grade systems at large scale; strong technical judgment in reliability, scalability, performance, and infrastructure efficiency; proficiency in at least one of C++, Java, or Python.
• Proven track record driving large-scale technical impact: experience leading reliability and scalability improvements, cost efficiency initiatives, and modernization efforts across critical production systems; able to define technical strategy, influence architecture decisions, and drive execution through cross-functional collaboration, strong communication, and operational excellence.
• Strong ownership, quality mindset, and thoughtful use of AI: demonstrates high standards for engineering quality, integrity, and accountability for final outcomes; able to use AI to accelerate analysis, debugging, design exploration, or operational workflows while applying critical thinking, validating correctness, and maintaining sound technical judgment rather than outsourcing ownership to tools.
• Exceptional collaboration skills with cross-functional partners, with the ability to navigate ambiguity, make tradeoffs, and keep stakeholders aligned on priorities and progress.
• Bachelor’s degree in Computer Science, a related technical field, or equivalent experience.
  
  

Relocation Statement: 

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

In-Office Requirement Statement: 

• We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
• This role will need to be in the office for in-person collaboration 1-2 times per quarter and therefore needs to be in a commutable distance from one of the following offices: PA or SF offices.

#LI-HYBRID

#LI-CL5

Senior Software Engineer, Energy Storage

This position is on a team building the software platform that powers optimized control and market participation of battery energy storage systems (BESS) — software that runs both on site controllers and in the cloud. Our scope spans the full stack: compute infrastructure, telemetry, asset modeling, alerting, feature ingestion and storage for forecasting and optimization, simulation and operational control orchestration, and integration with energy markets. We're a lean team that holds ourselves to high standards in both the quality of what we build and the speed at which we build it. We value collaboration, trust, learning, and engineering excellence, and we genuinely enjoy building great things together.

Responsibilities will include:

• Contribute to BESS data engineering — building and maintaining telemetry pipelines and stores, and feature ingestion and storage systems for forecasting and optimization
• Design, build, and maintain simulation and training infrastructure — platforms for running and orchestrating simulations at scale
• Develop and operate real-time forecast and optimization workflows, orchestrating pipelines that drive BESS operations
• Support energy market integration, including data ingestion and bid management
• Collaborate across the full stack and across team boundaries to ensure the product system works well as a whole
• Write well-tested, well-monitored software and iteratively improve it over time
• Apply AI tools to accelerate development while maintaining high system design quality and code standards

Desired Qualifications: 

• Strong software design instincts — thinks in systems, reasons from first principles, and understands the real-world challenges of building available, reliable, scalable, and secure distributed systems
• Comfortable using AI tools to move faster without letting them erode system design or code quality
• Excellent collaborator — communicates clearly, listens well, and makes the people around them better
• Bias toward action — takes initiative, makes progress in ambiguous situations, and doesn't wait for perfect information
• Systems thinker and owner — willing to think and collaborate across team boundaries to make sure the product works well as a whole
• Rigorous about quality — writes well-tested, well-monitored software and iterates on it over time
• B.S. or advanced degree in Software Engineering, Computer Science, or another discipline, combined with 2-5 years of experience in software engineering.
• Experience with relevant technologies a plus: Kubernetes, Rust, Python, NATS, PostgreSQL/TimescaleDB, SQLite
• A passion for sustainability and making the world a better place! 

Infrastructure Software Engineer, Energy Storage 

The Infrastructure Engineer is responsible for designing, building, and maintaining the foundational platform infrastructure that enables our engineering team to develop and deploy Battery Energy Storage Systems reliably and at scale. This role serves as a force-multiplier across the organization, owning edge fleet management, server provisioning, and deployment automation while ensuring systems are secure, scalable, and performant. The position partners closely with cross-functional engineering teams to translate early deployment learnings into platform improvements and drive resolution of scalability, reliability, and security challenges. Success in this role requires deep technical expertise, a collaborative mindset, and the ability to thrive in an ambiguous, fast-paced, early-stage environment. 

Responsibilities will include: 

• Design and own the rollout of edge Kubernetes fleet management, automated server provisioning, and software update pipelines to support scalable and reliable system deployment. 
• Evaluate server hardware and operating systems to help define the architecture of the organization's next-generation energy storage platform. 
• Identify and resolve hard infrastructure problems, including scalability bottlenecks, reliability gaps, performance issues, and security vulnerabilities. 
• Build and maintain tooling and services used to validate software systems at scale, including performance and reliability testing frameworks. 
• Serve as a technical resource for the broader engineering team through pair programming, in-office working sessions, and hands-on project leadership. 
• Translate learnings from early deployments and customer pilots into concrete, measurable improvements to platform infrastructure. 
• Reinforce a team culture grounded in engineering excellence, curiosity, inclusivity, and a willingness to take action and learn from mistakes. 

Desired Qualifications: 

• 5+ years of relevant industry experience in infrastructure, platform, or site reliability engineering. 
• Strong grasp of system design principles and real-world experience building available, reliable, scalable, and secure distributed systems. 
• Experience designing, implementing, and maintaining secure network systems and infrastructure. 
• Genuine interest in server hardware fundamentals and how software and hardware interact at the platform level. 
• Ability to work comfortably with ambiguity, prototype solutions iteratively, and learn rapidly from early-stage work. 
• Strong written and verbal communication skills, with the ability to explain complex systems clearly to both technical and non-technical audiences. 
• Ability to manage multiple workstreams, shifting priorities, and competing deadlines in a fast-paced, early-stage environment. 

The Role

Join us in revolutionizing the lending landscape. SoFi is seeking enthusiastic Senior Software Engineers who are ready to lead the development of key advancement to the next generation of our financial services platform. 

Data Foundations leads the path on building the central platform-level foundation and infrastructure that powers SoFi's entire data ecosystem. Their scope spans Infrastructure & Core Primitives and Platform Operations — essentially the backbone that enables all data capabilities across the organization. Key qualifications of this role include:

• Leading the technical evolution of one or more key platform subsystems.
• Resolving problems in the platform design and implementation that weren’t clearly anticipated, taking requirements with ambiguity and proposing durable technical solutions.
• Expertly leverage existing solution patterns and understand when to differentiate resulting in well-structured, maintainable code.
• Ability to understand data models, APIs, scaling concerns, and failure modes in order to make effective trade-offs between maintainability and performance.
• Creating stability by writing meaningful tests, considering observability, and leveraging your ability to debug complex issues across multiple system layers.
• The ability to create clear design and operational documentation.
• Contributes to code reviews, is a role model for more junior engineers, and contributes to team efficiency, beyond personal output.

What You’ll Do

As an emerging leader within our team, you will focus on building out the backend services for our next-generation Lending Platform. This role involves full product lifecycle engagement, from initial concept to deployment. Key responsibilities include implementing new capabilities, ensuring code quality, and delivering highly-available, scalable services. Success requires close technical collaboration with Product, Marketing, Operations, Business, Legal, and Compliance teams. You will be responsible for translating business requirements from these stakeholders into effective technical solutions that provide outstanding value to our members.

What You’ll Need

• Bachelor's or Master's degree in Computer Science, or Software Engineering
• 3+ years as a professional, full-time Software Engineer, or similar relevant work experience.
• Proficient coding in Java, Kotlin, or other similar object-oriented programming language.
• Proficient working with GIT and related CI/CD systems.
• Experience with public cloud compute, storage, and infrastructure.
• Experience with any data warehousing solution like Snowflake and IAC tools to provision platform infrastructure.
• Experience with Kafka, Docker, Kubernetes, and Spring Boot or similar frameworks.
• Deep understanding of relational and document database fundamentals.
• Deep understanding of fundamental distributed systems concepts.

Nice To Have

• Full-Stack experience in both back-end (Kafka, Redis, Docker) and front-end (JavaScript, TypeScript, React).
• Experience with microservices design principles and architecture.
• Experience with workflow orchestration technology (AWS step function, Airflow, Temporal).

We're driven to push boundaries and deliver exceptional products to help our members achieve financial stability. If you have the passion and a commitment to customer success, we want to hear from you.

The Team

Our engineering team builds systems that interact with some of the most complex software  ever deployed in production. The team is composed of industry veterans that have built deep learning infrastructure, autonomous drones, ridesharing marketplaces, ad tech and much more. 

We are looking for a client-obsessed AI Solutions Engineer with entrepreneurial tendencies to join the good fight and help build out our Solutions Engineering org. You’ll be the trusted technical advisors for our customers, driving business value, offering advice, and growing accounts. You’ll accomplish this by leading customers to solutions oftentimes by teaching the product to new users or consulting on best practices. You must be ready for technical discussions with data scientists and engineers, then demonstrate the value of Arize in business discussions with directors and executives. The goal is to enable our customers to become successful and enthusiastic about Arize. While we are a remote-first company, the nature of this role requires candidates to be based in the San Francisco Bay area/PST time zone.

What You’ll Do

• Work closely with some of the most sophisticated ML / GenAI teams in the world.
  • You will act as a trusted advisor to our customers, while also building relationships with technical and business stakeholders.
• Advise on GenAI and ML best practices 
• Give ML and LLM product demos to technical and business stakeholders
• Run strategic business reviews for customers in partnership with our sales team
• Interface with our pre-sales engineering team to gather client goals and KPI’s.
• Partner with our product and engineering teams to help drive the product roadmap
• Spearhead new opportunities within existing accounts to help drive expansions.

What We’re Looking For

Note: Even if you do not check every single box, we still encourage you to apply! 

• Previous experience working as a Data Scientist, Machine Learning Engineer, or as an Engineer working with ML models or GenAI applications in production.
• Comfortable working in public Cloud environments (AWS, Azure, GCP)
• Knowledge of machine learning frameworks such as TensorFlow, PyTorch or Scikit-learn
• Knowledge of LLM / Agentic frameworks such as Llamaindex, LangGraph, and DSPy
• Understanding of ML/DS concepts, model evaluation strategies and lifecycle (feature generation, model training, model deployment, batch and real time scoring via REST APIs) and engineering considerations
• Understanding of GenAI concepts and application evaluation + development lifecycle  
• Proficiency in a programming language (Python, JS/TS, Java, Go, etc)
• Strong Communication Skills - Ability to simplify complex, technical concepts.
• A quick and self learner - undaunted by technical complexity of production ML deployments and welcome the challenge to learn about them and develop your own POV.

Bonus Points, But Not Required

• Previous engineering experience in:
  • Data Science
  • MLOps
  • ML Frameworks
  • LLM / Agentic frameworks
• Customer facing experience strongly preferred such as Solutions Architect, Implementation Specialist, Sales Engineer, Customer Success Engineer, Consultant, or Professional Service roles
• Prior experience working with applications deployed with Kubernetes
• Prior experience demoing technical products to both business and technical audiences

The estimated annual salary and variable compensation for this role is between $125,000 - $175,000, plus a competitive equity package. Actual compensation is determined based upon a variety of job related factors that may include: transferable work experience, skill sets, and qualifications. Total compensation also includes a comprehensive benefit package, including: medical, dental, vision, 401(k) plan, unlimited paid time off, generous parental leave plan, and others for mental and wellness support.

While we are a remote-first company, we have opened offices in New York City and the San Francisco Bay Area, as an option for those in those cities who wish to work in-person. For all other employees, there is a WFH monthly stipend to pay for co-working spaces.

About this role

As a Staff Machine Learning Platform Engineer, you will help design, improve, and operate a scalable ML platform to accelerate model training, deployment, and governance. You are the technical bridge between data science and production engineering.  You’ll be joining a small but deeply critical team that scales Faire’s ability to support tens of thousands of local businesses in a constantly narrowing retail landscape.

What You Will Do

• Design and operate ML infrastructure, including workspaces, clusters, jobs, and workflows
• Productionize ML workloads using Spark, Delta Lake, MLflow, and Databricks Workflows
• Teach data scientists how to utilize our ML platform to advance development from notebook to production for our most critical models
• Implement Unity Catalog for data governance, lineage, access control, and secure multi-tenant usage
• Build CI/CD pipelines for ML using Terraform and Git-based workflows (e.g., GitHub Actions)
• Optimize performance, reliability, and cost across training and inference workloads
• Configure Identity and Access Management (IAM) and Role Based Authentication Controls (RBAC) for sensitive data sets
• Establish observability for data quality, model performance, and platform health
• Build and maintain ML Platform technical documentation

What it takes

• 8+ years of experience building production ML or data platforms
• A degree (preferably graduate level) in Computer Science, Engineering, Statistics, or a related technical field
• Strong hands-on expertise with Databricks, Spark, Delta Lake, and MLflow.
• Proficiency in Python, SQL, and distributed systems concepts
• Experience with cloud platforms and infrastructure-as-code
• Solid understanding of MLOps best practices: CI/CD, monitoring, reproducibility, and security
• Experience supporting multiple ML teams in a shared platform environment
• Are an active owner of orphaned problems and are willing to assimilate whatever knowledge you’re missing to get the job done

Tech Stack

Faire uses a modern cloud based tech stack.  For this role, you’ll want to be proficient with the following:

Salary Range

Canada: the pay range for this role is $216,000 to $297,000 per year. 

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.

Faire uses Artificial Intelligence (AI) to screen and select applicants for this position.

This job posting is for an existing vacancy.

Engineering at Brex

Engineering at Brex is about building systems that scale with speed and intention. Our teams span Software, Data, Security, and IT, and operate with high autonomy and deep collaboration. We tackle hard technical problems, own our outcomes, and push for excellence at every level — from architecture to deployment. It’s an environment where engineering is a craft, and builders become leaders.

What you’ll do

As the Engineering Manager for Cloud Infrastructure, you will lead a highly competent team of senior engineers responsible for the foundational AWS ecosystem that powers Brex. Your primary near-term mission will be driving our high-priority Business Continuity and Disaster Recovery (BCDR) initiative to completion, a company-level priority that involves multi-region failover and infrastructure-wide resilience. You will balance people leadership with deep technical involvement, earning the respect of a senior team while improving operational hygiene and cross-functional coordination across the entire engineering organization.

Where you’ll work

This role will be based in our Seattle office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week: Monday, Wednesday, and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Lead and Grow: Manage and support a senior team of 6 ICs, fostering a culture of high ownership, autonomy, and technical excellence.
• Drive BCDR Strategy: Oversee the execution of the multi-year BCDR initiative, ensuring multi-region infrastructure readiness and automated failover capabilities.
• Technical Stewardship: Maintain and scale Brex’s core cloud infrastructure, including Kubernetes (EKS), RDS/Aurora/Postgres, networking (VPC), and AWS service integrations.
• Cross-Functional Collaboration: Partner with product and engineering teams across Brex to drive adoption of new infrastructure capabilities and ensure successful migrations.
• Operational Excellence: Improve team hygiene by establishing and improving best practices for work tracking (Linear), documentation, and incident response.
• Resource Management: Effectively allocate resources to balance critical project delivery with long-term infrastructure stability.

Requirements

• Leadership Experience: 3+ years of experience directly managing software or infrastructure engineering teams.
• Technical Depth: Deep hands-on experience with cloud infrastructure foundations, specifically AWS, Kubernetes, and Postgres.
• Architectural Insight: Proven ability to design and implement complex infrastructure projects such as multi-region deployments or disaster recovery frameworks.
• Collaborative Mindset: Strong communication skills with the ability to influence technical decisions across multiple autonomous teams.
• Analytical Problem Solving: Ability to dive deep into technical issues while maintaining a high-level strategic view of organizational goals.
• Engineering Background: 5+ years of experience in software or systems engineering.

Bonus points

• Experience with Infrastructure as Code tools, specifically Terraform.
• Background in managing cloud infrastructure cost optimization programs.
• Experience with our tech stack components: Go, Kotlin, Python, or Elixir.
• Passion for leveraging AI and LLM-assisted workflows to increase engineering velocity.

Compensation

The expected salary range for this role is $240,000 - $300,000. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity. Depending on the position offered, equity and other forms of compensation may be provided as part of a total compensation package.

Brex LLC is a wholly owned subsidiary of Capital One, N.A.

About the Role

The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform, from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations.

Within Cloud Inference, the model & inference launch team owns the validation pipeline for our inference server and load balancer on these platforms. We're responsible for every inference change — model launches, performance improvements, safeguard integrations — landing on cloud platforms with correctness, performance, and reliability intact.

This is high-leverage infrastructure work: validation has to be fast and cheap enough to run on the same accelerators that serve customers, trustworthy enough to replace manual checks, and consistent enough that a change working on Anthropic first-party means it works everywhere. This directly determines how fast frontier models and features ship to every cloud platform, and how quickly performance wins reach production — reclaiming capacity at a time when compute is our scarcest resource.

What You'll Do

• Be on the critical path for frontier model launches, bringing up inference for new model architectures and shipping them to cloud platforms in lockstep with our first-party platform
• Work with the core inference team to bring new inference features (e.g. structured sampling, prompt caching, and more) to cloud platforms, owning the platform-specific integration that gets them to production
• Identify and dive deep on the gaps that make inference behave differently across first-party and CSPs — config drift, observability, deployment patterns, hard cross-platform bugs — and fix them at the source rather than building platform-specific workarounds
• Design, build, and own the CI/CD infrastructure for the inference server and load balancer across cloud platforms, with shadow traffic, performance baselines (throughput and latency), and correctness checks that catch regressions before production
• Drive down merge-to-production cycle time by making validation faster, more parallel, and cost-effective enough to run on the same constrained accelerator pool that serves customers, without trading away reliability 
• Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads

You May Be a Good Fit If You:

• Have a strong interest in LLM serving; prior inference or ML experience is not required 
• Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users
• Have a track record of building automation or test infrastructure that measurably improved release velocity or reliability
• Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code, or container orchestration
• Thrive in cross-functional collaboration with both internal teams and external partners
• Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems
• Are highly autonomous and take ownership of problems end-to-end, including work that falls outside your job description

Strong Candidates May Also Have Experience With:

• LLM inference optimization, batching, and caching strategies
• Capacity-constrained scheduling or shared-resource test infrastructure
• Solid understanding of multi-region deployments, request routing, load balancing, global traffic management
• Working with CSP partner teams to scale infrastructure across multiple platforms, navigating differences in networking, security, privacy, and managed service
• Proficiency in Python or Rust

Engineering at Brex

Engineering at Brex is about building systems that scale with speed and intention. Our teams span Software, Data, Security, and IT, and operate with high autonomy and deep collaboration. We tackle hard technical problems, own our outcomes, and push for excellence at every level — from architecture to deployment. It’s an environment where engineering is a craft, and builders become leaders.

What you’ll do

As a Security Operations Engineer at Brex, you will focus on preventing, detecting and responding to security threats across Brex's corporate and cloud environments. You will use existing systems and develop tools to improve our security capabilities. Our team is responsible for functions across corporate security, detection & response and infrastructure security domains; and we perform systems engineering and automation to support those functions. 

Security Operations is part of our wider Trust & IT organization which means you will have the opportunity to work closely with Application Security, Corporate Engineering, GRC and IT and to improve security configurations, drive positive employee behaviors and generally work to prevent events from becoming incidents. You will also help build and maintain our team’s open source project Substation and have the opportunity to contribute to the Brex Tech Blog. You’ll be part of a team that actively contributes to the wider security community and has a commitment to mentorship and engineering excellence.

We’re looking for individuals with a strong background and interest in detecting, responding to, and resolving security incidents and security challenges. You should be comfortable dealing with lots of moving pieces, changing priorities, and new technologies, while having a keen eye for detail. Most importantly, you should be enthusiastic about working with a variety of backgrounds, roles, and people across Brex. Building a world-class financial service requires world-class security.

Where you’ll work

This role will be based in our San Francisco office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Work on a highly cross-functional team to prevent, detect and respond to security threats across Brex's corporate and cloud environments
• Perform security incident response, investigation, remediation, and documentation, participate in periodic threat hunting and security exercises
• Leading, scoping and building features, participate in designing, and maintaining tools and systems which support the team’s domains – corporate security, detection & response and infrastructure security 
• Collaborating and partnering with engineering and operations teams to drive remediation of security issues, while balancing prioritization of those security issues within SLA and teams’ respective backlogs
• Caring about secure system design, valuing building things correctly, an understanding of a MVP approach and an empathetic mindset when working with others

Requirements

• Bachelor’s degree in Computer Science, Engineering or related field OR equivalent training / fellowship OR 5+ years work experienceExperience working in a corporate security, detection & response or infrastructure security role with responsibilities for security alert triage and security incident response 
• Familiarity with CI/CD systems and DevOps workflows (e.g. Buildkite, Flux, Git, Terraform) in cloud environments (e.g. AWS, Azure, GCP)
• Experience with deploying and maintaining some of the security services and tools owned by the team (e.g. - SIEM, data pipelines, SOAR, domain monitoring, endpoint tooling, email protection tooling, cloud security tools)
• While not primarily a development role, the team develops and maintains tools written in Go and Python, so experience with coding is required
• You thrive in a collaborative environment filled with a diverse group of people with different expertise and backgrounds. We currently have around 30 nationalities represented with more than ½ the company working in a country different from the one they grew up in.

Bonus points

• Proficiency with Go and other programming languages 
• Experience with securing distributed systems in AWS, cloud and Kubernetes environments
• Contributions to the wider technical community (open source, public research, mentorship, community organizing, blogging, presentations, etc)

Compensation

The expected salary range for this role is $192,000 - $240,000. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity. Depending on the position offered, equity and other forms of compensation may be provided as part of a total compensation package.

Brex LLC is a wholly owned subsidiary of Capital One, N.A.

The opportunity
Unity is looking for a Senior Machine Learning Infrastructure Engineer to join our Vector Ads team, where we build the real-time systems that power Unity's global advertising platform. This is a high-scale, low-latency environment — processing billions of requests daily to deliver fast, relevant ads to players around the world.

You'll build and operate the infrastructure that brings ML models from training into production, ensuring our ranking, bidding, and targeting systems run reliably at scale. This is a great opportunity for an engineer who's excited to work at the intersection of ML systems and distributed infrastructure, collaborate across teams, and have direct impact on how machine learning shapes the player and advertiser experience.

What you'll be doing

• Design, build, and maintain the infrastructure that serves ML models in real-time across Unity's ads ecosystem
• Build and operate scalable model serving pipelines — owning latency, throughput, and reliability in a high-QPS production environment
• Partner with ML engineers to productionize models, manage model deployments, and improve iteration speed
• Improve observability, performance, and cost-efficiency of ML serving infrastructure
• Contribute to architectural decisions around feature serving, model versioning, and inference optimization

What we're looking for

• Experience building and operating ML infrastructure or model serving systems in production
• Proficiency in Golang or Python, with strong systems engineering fundamentals
• Hands-on experience with Kubernetes and container orchestration at scale
• Familiarity with ML serving frameworks such as Ray Serve, Triton, TorchServe, or similar
• Understanding of distributed systems, API design, and system reliability
• Strong collaboration and communication skills in a remote-first environment

You might also have

• Experience with feature stores, feature pipelines, or online/offline feature serving
• Background in ad tech, real-time bidding, or programmatic advertising systems
• Familiarity with infrastructure-as-code such as Terraform
• Experience with observability tooling — Prometheus, Grafana, OpenTelemetry
• Background with real-time data pipelines, caching layers, or low-latency serving systems

Additional information

• Relocation support is not available for this position

Benefits
At Unity, we want our team members to thrive. We offer a wide range of benefits designed to support well-being and work-life balance.

Please note: Benefits eligibility, specific offerings, and coverage vary based on the country and employment status.

While specific benefits vary, here are some of the ways we strive to take care of our eligible team members globally: Comprehensive health, life, and disability insurance | Commute subsidy | Employee stock ownership | Competitive retirement/pension plans | Generous vacation and personal days | Support for new parents through leave and family-care programs | Office food snacks | Mental Health and Wellbeing programs and support | Employee Resource Groups | Global Employee Assistance Program | Training and development programs | Volunteering and donation matching program

Life at Unity
Unity [NYSE: U] is the world’s leading game engine, powering play for more than 3 billion consumers each month. The top mobile games in the world, the most played PC indie titles, the most innovative console games, and virtually all of the top XR and Web Games are developed, deployed, and grown in Unity. Unity also enables teams across industries like automotive, manufacturing, and healthcare to design, simulate, and collaborate in 3D — closing the gap between ideas and reality. For more information, please visit www.unity.com.

Unity is a proud equal opportunity employer. We are committed to fostering an inclusive, innovative environment and celebrate our employees across age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, or any other protected status in accordance with applicable law. Our differences are strengths that enable us to support the growing and evolving needs of our customers, partners, and collaborators. If you have a disability that means there are preparations or accommodations we can make to help ensure you have a comfortable and positive interview experience, please fill out this form to let us know.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

This position requires the incumbent to have a sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English.

Headhunters and recruitment agencies may not submit resumes/CVs through this Web site or directly to managers. Unity does not accept unsolicited headhunter and agency resumes. Unity will not pay fees to any third-party agency or company that does not have a signed agreement with Unity.

Your privacy is important to us. Please take a moment to review our Prospect Privacy Policy and Applicant Privacy Policy. Should you have any concerns about your privacy, please contact us at DPO@unity.com.

#SEN #LI-ED1

We are looking for a Staff Forward Deployed Engineer who is passionate about solving complex cloud infrastructure challenges in the fast-growing AI/ML space. This is a high-impact position designed to be the "technical tip of the spear" for our most strategic AI-Native (ANE) customers.

As an FDE, you will sit at the intersection of Product Engineering and Customer Implementation. You will be embedded directly with our most strategic customers to drive transformational AI adoption. You will collaborate closely with customer teams to ship advanced AI applications that solve real world business problems.

You will build the tools, migration scripts, and AI starter kits that don't just solve one customer's problem, but scale the entire DigitalOcean AI Cloud ecosystem. Your mission is to accelerate "time-to-inference" in production at scale and serve as a critical feedback loop, ensuring our product roadmap is informed by the world's most demanding AI workloads.

What you’ll do:

• Embedded Customer Engineering: Partner deeply with strategic ANEs to manage complex migrations, build production-ready PoCs, and execute hands-on application builds on our GPU infrastructure.
• Build Scalable Assets & Tooling: Operationalize field learning by building migration planners, reusable systems, benchmarking frameworks, model optimization agents, and deployment automation scripts (e.g., Terraform/Pulumi) that standardize how AI workloads are deployed and tuned across DigitalOcean.
• Develop AI "Starter Kits": Create pre-built GenAI agents, solution templates, and notebooks (e.g., LangGraph, CrewAI) tailored to popular and advanced business needs.
• Drive the Product Roadmap: Act as the frontline technical voice for CPTO, surfacing architectural gaps and edge cases to inform core product development. Transition field-built tools into native product features.
• New Tech Validation: Lead the early testing and integration of emerging AI frameworks to ensure a first-class developer experience on DigitalOcean.
• Partner & GTM Enablement: Co-develop delivery frameworks with Strategic and Technical partners to enable repeatable, high-quality deployments at scale.
• Market Leadership: Produce field-tested demo kits and deployment guides that support new product releases with practical, validated assets.

Key Metrics

• Customer Adoption: Number of high-impact production workloads landed; reduction in average "time-to-production"; and Pilot-to-Production conversion rates.
• Asset & Tooling Delivery: Number of reusable tools (agents, scripts, playbooks) delivered per quarter and their subsequent adoption rate by the crossfunctional teams and customers.
• Field Enablement & Scale: Measured by the successful handoff and execution of FDE-defined deployment patterns to technical partners and the internal adoption rate of FDE playbooks across all customer-facing technical teams.
• Product Influence: Number of roadmap features or architectural changes directly attributed to FDE feedback and validated customer hypotheses.

What You’ll Add to DigitalOcean:

• AI/ML Architecture & Domain Expertise: Significant experience in the AI/ML lifecycle, specifically hosting large language or multimodal models using inference engines like vLLM, SGLang, or Modular. You have a deep understanding of common LLM architectures and optimization techniques (e.g., continuous batching, quantization).
• Distributed Systems & Infrastructure Mastery: Act as the subject matter expert on modern GPU families (NVIDIA/AMD) and their software stacks (CUDA, ROCm, TensorRT, OpenAI Triton). Expert proficiency in Kubernetes (K8s) and the design of distributed systems, including microservices, messaging systems, databases, and Infrastructure as Code. You can seamlessly integrate AI workloads with core cloud services (Networking, VPC, Storage, Compute).
• High-Performance Inference & Hardware Optimization: Hands-on experience with distributed inference serving frameworks (e.g., llm-d, NVIDIA Dynamo, or Ray Serve). You understand GPU-level optimization and have experience with interconnect technologies like NVlink, XGMI, or RoCE to maximize hardware efficiency.
• Software Engineering Foundation: Strong production coding skills in Python or Go with the ability to build high-quality tools, automation, and internal assets.
• Data-Driven Performance Insight: Proven ability to benchmark AI infrastructure and perform GPU utilization tuning to optimize customer ROI and workload performance.
• Consultative Execution: Proven ability to establish instant technical credibility with CTOs and Lead Architects while managing high-stakes technical migrations.

Preferred

• Experience with emerging AI orchestration frameworks like LangGraph, CrewAI, or LlamaIndex.
• A background in technical consulting or "Forward Deployed" roles at high-growth AI or infrastructure companies.
• Experience building and scaling "Center of Excellence" models or partner delivery frameworks.
• Active contributor to open-source AI projects or technical communities.

Compensation Range: 

• $195,000 - $239,000

*This is a remote role

JR: 2026-7748

#LI-Remote

⛵️ About Wholesail

Wholesail is an early stage startup that builds software to connect wholesalers and their buyers by automating and digitizing invoicing and payment in the $5 trillion wholesale trade industry. The company’s first product is an electronic invoicing and payment portal for wholesale sellers, which allows them to get paid faster and decrease losses. For buyers, Wholesail automates the accounts payable & expense management process to save them time and money. Wholesail’s initial industry focus is to build a network effect driven payment business in the highly fragmented $1 trillion US food distribution market, in which invoicing and payment is nearly entirely paper-driven today. 

Wholesail was founded by a team of engineers, product managers, and designers whose prior startups were acquired by OpenTable, and the company is well funded with backing from a half dozen VCs and 20+ angel investors including early executives and team members from Stripe, Square, Dropbox, Yelp, OpenTable and Everlane.

Are you fascinated by how people use products, actively seek to understand the “why” behind product decisions, and approach product building as a craft? Are you passionate about working with other experienced engineers who have a passion for their craft? We'd love to have you join our voyage as a founding member of our product engineering team.  

🛠 About the Role

As an early engineer, you’ll build new features and internal APIs to create value for the suppliers and buyers on our network. Your work will skew towards backend development, but you'll end up working up and down the stack and you’ll spend a lot of time with our sales and support team and our customers to figure out the details of the products you craft. Our small product team wears many hats, so you'll get significant exposure to many areas adjacent to engineering: design, marketing, customer success, sales, and all of the functions of an early stage startup.

As a product oriented member of our engineering team, you’ll couple strong technical execution with an active voice in shaping the product through contribution in product conversations and identifying product/technical tradeoffs. You’ll participate in different styles of execution based on the stage of product development – from iterating quickly on an experiment with intentionally scrappy code to building sustainable systems that serve as a foundation for years to come.

Most significantly, you'll be building the company with us. We are early on our voyage and there are vast uncharted seas ahead. We want to work with fellow Wholesailers who care deeply about the process of building an enduring company: from what values we embrace, how we build our teams, decisions about how we grow our business, and everything in between.

🚀  You’d be great a great fit for our voyage if you… 

• Are naturally curious about and have a keen interest in understanding the "why?" by digging into the nuts-and-bolts of how things work as a first principles thinker.
• Think deeply about products – from building empathy for users, to understanding how a product works, how it fits into a complex system, and how it creates business value.
• Obsess about the details of your craft whether it’s designing a new abstraction layer, building an API for a colleague on the frontend team or shipping a pixel perfect feature.
• Quickly build intuition in new domains and are able to articulate product/engineering tradeoffs early on while also pragmatically handling (and minimizing work on) edge cases.
• Estimate and prioritize when faced with different proposed solutions, to help guide the team towards building the most impactful products.
• Strive to quickly deliver value through rapid product validation cycles and have a keen ability to identify quick wins that create value for customers.
• Use data to guide your understanding of user behavior and inform the decisions you make to maximize building value for customers.
• Strive for end-to-end ownership of your work, are proactive with sharing product ideas/opinions, and seek early feedback from other members of your team.
• Build empathy and relationships with strong listening, written, and verbal communication skills – particularly with non-engineering folks such as designers, PMs, support and sales.

🤝 Engineering Culture & Environment

At Wholesail, we started as a team of senior engineers who are passionate about using technology to build products and tools to improve the lives of buyers and sellers in the wholesale industry. Many of us have a background working on technologies used in the relevant food and payments industries, and together with our cross-functional partners, we strive to learn about the problems that our customers in the wholesale industry are facing, so we can work hard to provide solutions for our buyers and sellers.  

Wholesail provides products that help our customers make payments and process orders, which are essential to our customers' business, and therefore we have a strong focus on security, reliability and system performance. We emphasize good engineering practices, and adopt test-driven development and continuous deployment to iterate quickly and confidently. We identify and eliminate or mitigate the impact of single points of failure in our systems. We also genuinely take pride in the craftsmanship of the code we produce, and we love to work with colleagues whose work we respect. Intermixed with all of this is a sense of good humor, and an earnest enjoyment in working together.

Wholesail’s tech stack starts with Java services running in Kubernetes (GKE on Google Cloud Platform), with PostgreSQL as the database. The frontend is a React-Native single page application that is served statically.  The Wholesail platform integrates with third party services and APIs, including Plaid, Stripe, Modern Treasury and QuickBooks, among other systems. Given the financial and accounting nature of the product, the company has invested in extensive testing infrastructure. In addition to more traditional automated testing, we built orchestration that provisions independent copies of the entire service architecture with candidate releases to test the system, end-to-end, before rolling out new changes.

We want people who are passionate about troubleshooting complex problems with systems, networking and storage at scale.

We are looking for a seasoned system administrator to help us keep the cloud running smoothly. Reporting to the manager of Cloud Operations, the GPU Operations Engineer monitors and provides first-response to all cloud health issues that impact, or could potentially impact, customer experience - internal or external. You will interface with teams across the organization to research and troubleshoot issues from single droplets to cloud-wide disturbances. Our workweek spans five days, and that may involve working on weekends.

What You'll Be Doing: 

• Ensuring maximum uptime for our global infrastructure
• Automating processes and building tools to improve operational efficiency
• Coordinating operational work across teams to improve the platform with minimal impact

What You'll Add to DigitalOcean:

• Solid experience with Linux operating systems or Networking and day to day upkeep
• Familiarity with virtualization technologies and troubleshooting virtual machine instances
• Familiarity with containerization technologies and troubleshooting containers
• Familiarity with IPv4 Networking and troubleshooting (CCNA equivalent)
• Basic storage concepts and technologies
• Experience with monitoring systems and incident management
• Experience scripting in one or more of the following languages: Bash, Python, or Go
• Experience with GPU hardware or AI/ML, and Kubernetes
• A passion for good documentation and open communication
• Proven ability to learn!

Compensation Range: 

• $107,000 - $134,000

*This is a remote role

JR: 2026-7423

#LI-Remote

Location: This position can work onsite or hybrid from San Francisco Bay Area. 
Travel: Up to 15%, domestic or international.
Job ID: 1558

The Role: 

We are looking for a Senior Staff Software Architect to lead the design and evolution of our next-generation Network Monitoring Platform. You’ll be part of a cross-functional team whose mission is to lead IonQ on its journey to build the world’s best quantum computers to solve the world’s most complex problems.

In this role, you will own the end-to-end architecture of a high-scale, data-intensive network monitoring platform — from data modeling and ingestion pipelines to cloud-native deployment. You are a hands-on technical leader who writes production code, drives critical architectural decisions, and has deep experience building systems that monitor large fleets of devices at scale. The ideal candidate brings strong Go and/or Rust expertise and thrives at the intersection of systems engineering and distributed data platforms.

Responsibilities:

• Architect cloud-native ingestion and processing pipelines capable of handling telemetry from hundreds of thousands of devices, leveraging streaming platforms and time-series storage at scale.
• Define and own the platform’s unified YANG-based data model, spanning device configuration state, operational telemetry, and topology relationships across heterogeneous network environments, serving as the authoritative schema from which ingestion pipelines, storage structures, and API contracts are derived.
• Write and review production-quality code in Go and/or Rust for performance-critical platform components including collectors, aggregators, and real-time processing engines.
• Define and enforce data partitioning, sharding, and retention strategies to sustain high-volume device event streams while meeting latency and durability requirements.
• Lead the design of cloud-native deployment patterns (Kubernetes, service mesh, autoscaling) to ensure the platform operates reliably at global scale across multi-cloud environments.
• Evaluate and drive adoption of purpose-fit technologies — time-series databases, graph stores, stream processors — with a focus on operational scalability and long-term maintainability.
• Partner with Product and Engineering to translate monitoring requirements into platform capabilities, ensuring architectural decisions are reflected in roadmap milestones and engineering specifications.
• Mentor senior engineers through architecture reviews, coding standards, and hands-on technical guidance, raising the bar for platform quality and engineering practices.

Requirements:

• 12+ years of experience in software or platform architecture, with at least 5 years leading architecture for large-scale, data-intensive distributed systems.
• Substantial experience building and operating device monitoring systems at scale — with direct exposure to telemetry ingestion pipelines, fleet management, and real-time alerting across large device populations.
• Proven hands-on coding ability; production experience with Go and/or Rust is strongly preferred for this role.
• Deep expertise in data modeling for high-volume systems, including schema design, time-series storage (InfluxDB/Timescale), event streaming (Kafka/Kinesis), and graph data models for network topology.
• Solid understanding of cloud-native application design — including containerization, Kubernetes orchestration, microservices, and cloud infrastructure across AWS, GCP, or Azure.

Preferred Qualifications:

• Proficiency in both Go and Rust, with a track record of shipping production services in at least one of these languages.
• Background in network management platforms or network equipment vendors (e.g., Juniper, Cisco, Nokia), with familiarity with telemetry protocols such as gNMI, NETCONF, or SNMP.
• Experience designing multi-tenant SaaS platforms where data isolation, schema evolution, and scalability across customer tiers are first-class concerns.
• Contributions to open-source infrastructure or observability projects, or published technical writing on distributed systems and data engineering topics.
• Familiarity with techniques relevant to high-throughput data collection.

The approximate base salary range for this position is $216,136 - $282,978. The total compensation package includes base, bonus, equity, and a range of benefit options found on our career site.

About this role

As a Staff Machine Learning Platform Engineer, you will help design, improve, and operate a scalable ML platform to accelerate model training, deployment, and governance. You are the technical bridge between data science and production engineering.  You’ll be joining a small but deeply critical team that scales Faire’s ability to support tens of thousands of local businesses in a constantly narrowing retail landscape.

What You Will Do

• Design and operate ML infrastructure, including workspaces, clusters, jobs, and workflows
• Productionize ML workloads using Spark, Delta Lake, MLflow, and Databricks Workflows
• Teach data scientists how to utilize our ML platform to advance development from notebook to production for our most critical models
• Implement Unity Catalog for data governance, lineage, access control, and secure multi-tenant usage
• Build CI/CD pipelines for ML using Terraform and Git-based workflows (e.g., GitHub Actions)
• Optimize performance, reliability, and cost across training and inference workloads
• Configure Identity and Access Management (IAM) and Role Based Authentication Controls (RBAC) for sensitive data sets
• Establish observability for data quality, model performance, and platform health
• Build and maintain ML Platform technical documentation

What it takes

• 8+ years of experience building production ML or data platforms
• A degree (preferably graduate level) in Computer Science, Engineering, Statistics, or a related technical field
• Strong hands-on expertise with Databricks, Spark, Delta Lake, and MLflow.
• Proficiency in Python, SQL, and distributed systems concepts
• Experience with cloud platforms and infrastructure-as-code
• Solid understanding of MLOps best practices: CI/CD, monitoring, reproducibility, and security
• Experience supporting multiple ML teams in a shared platform environment
• Are an active owner of orphaned problems and are willing to assimilate whatever knowledge you’re missing to get the job done

Tech Stack

Faire uses a modern cloud based tech stack.  For this role, you’ll want to be proficient with the following:

Salary Range

San Francisco: the pay range for this role is $246,500 to $339,000 per year. 

This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.

Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform that provides APIs for knowledge retrieval, inference, evaluation, and more. We are looking for a strong engineer to join our team and help us build and scale our product in a fast-paced environment. The ideal candidate will have a strong understanding of software engineering principles and practices, as well as experience with large-scale distributed systems. You will be responsible for owning large new areas within our product, working across backend, frontend, and interacting with LLMs and ML models. You will solve hard engineering problems in scalability and reliability.

You will:

• Own large new areas within our product
• Work across backend, frontend, and interacting with LLMs and ML models
• Deliver experiments at a high velocity and level of quality to engage our customers
• Work across the entire product lifecycle from conceptualization through production
• Be able, and willing, to multi-task and learn new technologies quickly

Ideally you'd have:

• 7+ years of full-time engineering experience, post-graduation
• Experience scaling products at hyper growth startups
• Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies
• Proficient in Python or Javascript/Typescript, and SQL
• Experience with Kubernetes
• Experience with major cloud providers (AWS, Azure, GCP)

Scale GP (Scale Generative AI Platform) is an enterprise-grade AI platform that provides APIs for knowledge retrieval, inference, evaluation, and more. We are looking for a strong engineer to join our team and help us build and scale our core infrastructure in a fast-paced environment. 

The ideal candidate will have a strong understanding of software engineering principles and practices, as well as experience with large-scale distributed systems. You will implement solutions across multiple cloud providers (GCP, Azure, AWS) for customers in diverse, highly-regulated industries like healthcare, telecom, finance, and retail.

What You’ll Do:

• Architect multi-cloud systems and abstractions to allow the SGP platform to run on top of existing Cloud providers
• Implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs)
• Collaborate with platform, product teams and our customers directly to develop and implement innovative infrastructure that scales to meet evolving needs.
• Deliver experiments at a high velocity and level of quality to engage our customers
• Work across the entire product lifecycle from conceptualization through production
• Be able, and willing, to multi-task and learn new technologies quickly

What We’re Looking For:

• 4+ years of full-time engineering experience, post-graduation
• Experience scaling products at hyper growth startups
• Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies
• Proficient in Python or Javascript/Typescript, and SQL
• Experience with Kubernetes
• Experience with major cloud providers (AWS, Azure, GCP)
• Excellent communication skills with the ability to explain technical concepts to both technical and non-technical audiences

As a Software Engineer on the ML Infrastructure team, you will design and build platforms for scalable, reliable, and efficient serving of LLMs. Our platform powers cutting-edge research and production systems, supporting both internal and external use cases across various environments.

The ideal candidate combines strong ML fundamentals with deep expertise in backend system design. You’ll work in a highly collaborative environment, bridging research and engineering to deliver seamless experiences to our customers and accelerate innovation across the company.

You will:

• Build and maintain fault-tolerant, high-performance systems for serving LLMs workloads at scale.
• Build an internal platform to empower LLM capability discovery.
• Collaborate with researchers and engineers to integrate and optimize models for production and research use cases.
• Conduct architecture and design reviews to uphold best practices in system design and scalability.
• Develop monitoring and observability solutions to ensure system health and performance.
• Lead projects end-to-end, from requirements gathering to implementation, in a cross-functional environment. 

Ideally you'd have:

• 5+ years of experience building large-scale, high-performance backend systems.
• Strong programming skills in one or more languages (e.g., Python, Go, Rust, C++).
• Experience with LLM serving and routing fundamentals (e.g. rate limiting, token streaming, load balancing, budgets, etc.)
• Experience with LLM capabilities and concepts such as reasoning, tool calling, prompt templates, etc.
• Experience with containers and orchestration tools (e.g., Docker, Kubernetes).
• Familiarity with cloud infrastructure (AWS, GCP) and infrastructure as code (e.g., Terraform).
• Proven ability to solve complex problems and work independently in fast-moving environments.

Nice to haves:

• Experience with modern LLM serving frameworks such as vLLM, SGLang, TensorRT-LLM, or text-generation-inference.

Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform that provides APIs for knowledge retrieval, inference, evaluation, and more. We are looking for a strong engineer to join our team and help us build and scale our product in a fast-paced environment. The ideal candidate will have a strong understanding of software engineering principles and practices, as well as experience with large-scale distributed systems. You will be responsible for owning large new areas within our product, working across backend, frontend, and interacting with LLMs and ML models. You will solve hard engineering problems in scalability and reliability.

You will:

• Own large new areas within our product
• Work across backend, frontend, and interacting with LLMs and ML models
• Deliver experiments at a high velocity and level of quality to engage our customers
• Work across the entire product lifecycle from conceptualization through production
• Be able, and willing, to multi-task and learn new technologies quickly

Ideally you'd have:

• 4+ years of full-time engineering experience, post-graduation
• Experience scaling products at hyper growth startups
• Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies
• Proficient in Python or Javascript/Typescript, and SQL
• Experience with Kubernetes
• Experience with major cloud providers (AWS, Azure, GCP)

Senior Software Engineer, Platform — Connectivity

The Connectivity team owns the core platform that enables Scale to continuously generate, evaluate, and deliver high-quality training data to our customers. Our systems sit at the center of Scale’s data and AI infrastructure, powering large-scale workflows while supporting frontier AI capabilities such as multi-modality, long-horizon reasoning, and agentic behavior.

We’re looking for a senior engineer with deep experience building robust platforms, someone who brings strong architectural judgment, a bias toward correctness and reliability, and the ability to design systems that scale and evolve cleanly over time. This is a backend-heavy role with full-stack expectations and significant ownership from design through long-term operation.

What You’ll Do

• Design and own foundational platform systems that support scalable data generation, evaluation, and bespoke customer delivery across Scale’s ecosystem.
• Architect extensible, production-grade services that can support frontier AI workflows, including multi-modal inputs, long-running processes, and agentic orchestration.
• Build and operate distributed systems at scale, with strong guarantees around correctness, reliability, observability, and cost efficiency.
• Integrate with public LLM APIs and AI services, designing abstractions that are resilient to model churn, latency variability, and evolving usage patterns.
• Design and maintain data transformation and processing systems, supporting complex schema evolution, customization, and high-throughput workloads.
• Partner closely with infrastructure, product, and customer-facing teams to define requirements, shape technical direction, and deliver seamless integration experiences for customers.
• Lead multi-quarter technical initiatives, including authoring and driving a 6+ month technical roadmap for major platform investments.
• Apply strong engineering judgment in ambiguous problem spaces, balancing speed with long-term maintainability and operational excellence.
• Raise the quality bar through thoughtful system design reviews, rigorous code reviews, and mentorship grounded in real-world production experience.

Ideal Experience

• 7+ years of professional software engineering experience, with a strong background in building and operating large-scale, production-grade platforms.
  
  
• Deep expertise in distributed systems and cloud-native architectures, including Kubernetes, microservices, event-driven systems, caching, and production databases.
  
  
• Proven ability to lead multi-quarter technical initiatives, work effectively across cross-functional teams, and apply strong architectural judgment in ambiguous environments.
  
  
• Preferred: Experience integrating with public LLM APIs and designing systems that handle scale, reliability, latency, and cost tradeoffs.
  
  

Preferred: Experience building or operating data processing and transformation systems supporting complex workflows and evolving schemas.

The Public Sector software engineers (SWEs) create the core product building blocks forward-deployed teams use to develop agentic capabilities that function across multiple domains. SWEs responsibilities include building the systems required to ingest and process federal datasets to support real-time decision-making in contested environments. We develop novel agentic enabling capabilities that includes:

• Create multi-layered guardrails around agents
• Optimize data retrieval for agents
• Orchestrate fleets of asynchronous agents
• Automatically alerts users to deviations in data
• Illustrating how an agent reached a decision

As a Staff Software Engineer, you will orchestrate the implementation of vertical features and horizontal capabilities to include mentoring other engineers on defining requirements with stakeholders and communication tradeoffs of technical implementations on feature and capabilities until they are accepted by the stakeholders.

You will: Orchestrate feature implementation across the Federal engineering team to ensure architectural consistency.

• Define technical strategy for agentic guardrails, explainability, and fleet orchestration.
• Ensure system reliability and performance across multiple security classifications and network types.
• Mentor engineers in the process of defining requirements with stakeholders and gathering acceptance.
• Communicate high-level technical trade-offs and implementation strategies to senior government stakeholders and Scale C-Suite members.
• Influence the long-term product strategy and technical roadmap for the Federal business unit.
• Consult on the architecture of AI-powered solutions for large-scale federal contracts.

Ideally you will have: 

• Full Stack Development: Proficiency in front-end, back-end development and infrastructure, including experience with modern web development frameworks, programming languages, and databases
• Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is a plus
• Data Engineering: Knowledge of ETL (Extract, Transform, Load) processes and experience in building data pipelines to integrate and process diverse data sources. Understanding of data modeling, data warehousing, and data governance principles
• AI Application Integration: Familiarity with integrating Large Language Models (LLMs) and building agentic workflows. Understanding of prompt engineering, retrieval-augmented generation (RAG), and agent orchestration is beneficial.
• Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles
• Collaboration and Communication: Excellent interpersonal and communication skills to effectively collaborate with cross-functional teams, stakeholders, and customers. Ability to clearly articulate technical concepts to non-technical audiences and foster a collaborative work environment
• Adaptability and Learning Agility: Willingness to embrace new technologies, learn new skills, and adapt to defining and evolving project requirements. Ability to quickly grasp and apply new concepts and stay up-to-date with emerging trends in software engineering

Scale's Physical AI business unit is dedicated to solving the data bottleneck across Robotics, Autonomous Vehicles, and Computer Vision. This position will be a key contributor in conducting applied research in Physical AI and developing ML pipelines for processing, training, and fine-tuning on data collected by Scale, with a specific focus on optimizing algorithms and pipelines to run efficiently on GPUs in the cloud. In this role, you will have the opportunity to advance research, shape Scale’s offerings, and expand the frontier of data and model evaluation for Physical AI.

The Role 

As an ML Systems Engineer on the Physical AI team, you will design and build platforms for scalable, reliable, and efficient serving of foundation models specifically tailored for physical agents. Our platform powers cutting-edge research and production systems, supporting both internal research discovery and external customer use cases for autonomous vehicles and robotics.

The ideal candidate combines strong ML fundamentals with deep expertise in backend system design. You’ll work in a highly collaborative environment, bridging the gap between Physical AI research and production engineering to accelerate innovation across the company.

You Will: 

• Build & Scale: Maintain fault-tolerant, high-performance systems for serving robotics-related models and foundation models at scale, ensuring low latency for real-time applications.
• Platform Development: Build an internal platform to empower model capability discovery, enabling faster iteration cycles for research teams working on robotics.
• Collaborate: Work closely with Robotics researchers and Computer Vision engineers to integrate and optimize models for production and research environments.
• Design Excellence: Conduct architecture and design reviews to uphold best practices in system scalability, reliability, and security.
• Observability: Develop monitoring and observability solutions to ensure system health and real-time performance tracking of model inference.
• Lead: Own projects end-to-end, from requirements gathering to implementation, in a fast-paced, cross-functional environment.

Ideally, You’d Have: 

• Experience: 4+ years of experience building large-scale, high-performance backend systems, with deep experience in machine learning infrastructure.
• Algorithm Optimization: Deep experience optimizing computer vision and other machine learning algorithms for cloud environments, including GPU-level algorithm optimizations (e.g., CUDA, kernel tuning).
• Programming: Strong skills in one or more systems-level languages (e.g., Python, Go, Rust, C++).
• Systems Fundamentals: Deep understanding of serving and routing fundamentals (e.g., rate limiting, load balancing, compute budgets, concurrency) for data-intensive applications.
• Infrastructure: Experience with containers (Docker), orchestration (Kubernetes), and cloud providers (AWS/GCP).
• IaC: Familiarity with infrastructure as code (e.g., Terraform).
• Mindset: Proven ability to solve complex problems and work independently in fast-moving environments.

Nice to Haves: 

• Exposure to Vision-Language-Action (VLA) models.
• Knowledge of high-performance video processing (e.g., FFmpeg, NVDEC/NVENC) or 3D data handling (point clouds).
• Familiarity with robotics middleware (e.g., ROS/ROS2) or AV data formats.

Scale AI is seeking a highly skilled and motivated Software Engineer, Frontier AI Infrastructure to join our dynamic Public Sector Engineering team. As a part of this team, you will own the model inference layer - enabling state of the art models, debugging the latest AI tools, managing networking, debugging latency, and tracking pricing/usage metrics for AI models. You will lead technical discussions on the frontlines with cloud vendors and customers to deliver on critical contracts and to debug platform issues. You will also work upstream with Product to understand features before they break, moving us from "infra-only debugging" to proactive integration testing.

You will:

• Design and implement secure scalable backend systems for Public Sector customers, leveraging Scale's modern and cloud-native AI infrastructure.
• Own services or systems and define their long-term health goals, while also improving the health of surrounding components
• Re-architect the stack to run in compliant or restrictive environments. This requires designing swappable components (auth, storage, logging) to meet government/security mandates without breaking the product.
• You will work with Product to build integration tests that catch issues early, shifting the focus from "infra-only debugging" to preventing failures upstream.
• Participate actively in customer engagements, working closely with stakeholders to understand requirements and deliver innovative solutions.
• Contribute to the platform roadmap and product strategy for Scale AI's Public Sector business, playing a key role in shaping the future direction of our offerings.

Must have:

• At least an active secret clearance and the ability & willingness to up level to TS/SCI with CI Poly. This is a requirement and candidates will not be considered who do not hold at least a secret clearance

Ideally you'd have: 

• Full Stack Development: Proficiency in both front-end and back-end development, including experience with modern web development frameworks, programming languages, and databases. Experience with developing & delivering software to air-gapped & isolated environments is a plus.
• Cloud-Native Technologies: Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is desired. Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. 
• Security Focused: Experience with Federal Compliance frameworks, and requirements(e.g, Cloud SRG, FedRAMP, STIG Benchmarks, etc). Experience developing software & technical solutions that meet strict security & regulatory compliance requirements.
• Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles.
• Collaboration and Communication: Excellent interpersonal and communication skills to effectively collaborate with cross-functional teams, stakeholders, and customers. Ability to clearly articulate technical concepts to non-technical audiences and foster a collaborative work environment.
• Adaptability and Learning Agility: Willingness to embrace new technologies, learn new skills, and adapt to evolving project requirements. Ability to quickly grasp and apply new concepts and stay up-to-date with emerging trends in software engineering.
• Must be able to support work 3-4 days a week from the DC, SF, NYC, or STL office.

• Conduct in-depth code reviews to identify and remediate security vulnerabilities.
• Evaluate and enhance the security of our product offerings, through RFC and service review.
• Implement and maintain CI/CD pipelines with a strong focus on security.
• Perform Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST) to identify vulnerabilities in production code.
• Utilize terraform orchestration to ensure secure and efficient infrastructure management.
• Guide engineering teams to build robust long-term solutions that consider security and privacy.
• Clearly explain the mechanics and significance of security vulnerabilities, including their exploitability and potential impact.
• Influence the security strategy and direction of the team, advocating for best practices and continuous improvement.

• Proven experience as a Security Engineer with a focus on product security.
• Proficiency in NodeJS, TypeScript, Python, and/or Kubernetes.
• Strong understanding of modern Javascript application design.
• Production experience with Kubernetes backed services
• Hands-on experience with SAST and DAST tools and methodologies.
• Familiarity with terraform orchestration for infrastructure management.
• You can structure complex problems and diagnose root causes independently, providing actionable insights without requiring manager input.
• Excellent communication skills, with the ability to clearly present technical concepts and their implications to both technical and non-technical stakeholders.
• Demonstrated ability to influence security strategies and drive improvements within a team.
• Relevant security certifications (e.g., CISSP, CEH, OSCP) are a plus.

Scale AI is seeking a highly skilled and motivated Mission Software Engineer to join our dynamic Federal Engineering team. As a part of this team, you will play a critical role in supporting Scale’s government customers by scoping and developing onsite solutions. Our scalable, high-performance platform is the foundation for these customer solutions, and your expertise will be instrumental in designing and implementing systems that can handle interactions with existing customer systems to help our products integrate into existing customer workflows.   

You will:

• Work directly with customers to understand their problems and translate those into features in Scale’s platform.
• Be open to >50% travel or relocation to a key customer geographic location.
• Collaborate with cross-functional teams to define and execute the vision for backend solutions, ensuring they meet the unique needs of government agencies operating in secure environments.
• Implement end-to-end data integrations, syncing customer’s data to Scale’s platform and back
• Deploy and maintain Scale software at customer sites
• Develop customer requested features and work closely with them to ensure that they win customer love.
• Build robust and reliable backend systems that can serve as standalone products, empowering customers to accelerate their own AI ambitions.
• Participate actively in customer engagements, working closely with stakeholders to understand requirements and deliver innovative solutions.
• This role will require an active TS/SCI security clearance or the ability to obtain a security clearance.

Ideally you have:

• Track record of success as a hybrid customer facing engineer, forward deployed software engineer, and ability to quickly adapt to different roles.
• Prior experience developing with Python and JavaScript, or other modern software languages. Familiarity with Node and React is a plus.
• Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is a plus
• Linux experience: Understanding of shell scripting, operating systems, etc
• Networking experience: Understanding of networking technologies, configuration  (ports, protocols, etc) 
• Data Engineering: Knowledge of ETL (Extract, Transform, Load) processes and experience in building data pipelines to integrate and process diverse data sources. Understanding of data modeling, data warehousing, and data governance principles
• Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles

Scale AI’s Public Sector business is growing quickly as government agencies adopt AI to support critical national security, defense, and public sector missions. We are looking for a Mission Software Engineering Manager to join our dynamic Federal Engineering team. As a part of this team, you will play a critical role in supporting Scale’s government customers by scoping and developing onsite solutions. Our scalable, high-performance platform is the foundation for these customer solutions, and your expertise will be instrumental in designing and implementing systems that can handle interactions with existing customer systems to help our products integrate into existing customer workflows.  

You will:

• Recruit a high-performing engineering team. 
• Drive engineering productivity. Provide guidance, mentorship, and technical leadership to a team of engineers working on Generative AI projects. 
• Collaborating with cross-functional teams to define, design, and execute strategic roadmap.
• Work directly with customers to understand their problems and translate those into features in Scale’s platform.
• Be open to ~25% travel or relocation to a key customer geographic location.
• Collaborate with cross-functional teams to define and execute the vision for backend solutions, ensuring they meet the unique needs of government agencies operating in secure environments.
• Implement end-to-end data integrations, syncing customer’s data to Scale’s platform and back.
• Deploy and maintain Scale software at customer sites
• Develop customer requested features and work closely with them to ensure that they win customer love.
• Build robust and reliable backend systems that can serve as standalone products, empowering customers to accelerate their own AI ambitions.
• Participate actively in customer engagements, working closely with stakeholders to understand requirements and deliver innovative solutions.
• This role will require an active TS/SCI security clearance or the ability to obtain a security clearance.

Ideally you’d have:

• 5+ years of full-time engineering experience, post-graduation
• 2+ years of prior engineering management or equivalent experience and has managed an engineering team.
• Track record of success as a hybrid customer facing engineer, forward deployed software engineer, and ability to quickly adapt to different roles.

• Prior experience developing with Python and JavaScript, or other modern software languages. Familiarity with Node and React is a plus.
• Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is a plus
• Linux experience: Understanding of shell scripting, operating systems, etc
• Networking experience: Understanding of networking technologies, configuration  (ports, protocols, etc) 
• Data Engineering: Knowledge of ETL (Extract, Transform, Load) processes and experience in building data pipelines to integrate and process diverse data sources. Understanding of data modeling, data warehousing, and data governance principles
• Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles
• Understand unique DoD and USG constraints when it comes to technology

As a Software Engineer on the Machine Learning Infrastructure team, you will build the "Operating System" for our large-scale GPU clusters. You will architect a high-performance training platform that handles the immense complexity of multi-thousand GPU workloads, ensuring every cycle is used efficiently. Your work directly determines the velocity at which our researchers can train and iterate on the world’s most advanced models.

The ideal candidate is a systems expert who thrives on solving the orchestration, networking, and reliability challenges that emerge at massive scale. You will partner closely with researchers to build a seamless, resilient environment that transforms raw compute into breakthrough AI.

You will:

• Architect and scale a multi-tenant orchestration layer that abstracts away the complexity of GPU clusters, ensuring high utilization and seamless job recovery.
• Design and implement scheduling primitives to optimize the lifecycle of training jobs.
• Develop deep observability and automated health-checking into the training stack to proactively identify and isolate hardware failures
• Evaluate and integrate emerging technologies in the CNCF and AI ecosystem (e.g. Ray, Kueue), making data-driven build vs. buy decisions that balance velocity with long-term maintainability.
• Work closely with Finance and Procurement teams to drive our capacity planning process.
• Participate in our team’s on call process to ensure the availability of our services.
• Own projects end-to-end, from requirements, scoping, design, to implementation, in a highly collaborative and cross-functional environment.

Ideally you'd have:

• 5+ years of experience in backend or infrastructure engineering, with at least 2 years focused on orchestrating ML workloads at scale (100+ GPU nodes).
• Strong programming skills in one or more languages (e.g. Python, Go, Rust, C++)
• Experience with complex compute management systems that cover queueing, quotas, preemption, and gang scheduling.
• Experience with distributed training infrastructure, such as EFA, Infiniband, and topology-aware scheduling.
• Experience with distributed storage systems (e.g. Lustre, S3) as they relate to training throughput
• Expert-level knowledge of Kubernetes internals (Custom Resources, Operators, Admission Controllers) and how they interact with device plugins for specialized hardware.
• Familiarity with cloud infrastructure (AWS, GCP) and infrastructure as code (e.g., Terraform).
• Proven ability to solve complex problems and work independently in fast-moving environments.

Nice to haves:

• Experience with distributed training techniques such as DeepSpeed, FSDP, etc.
• Experience with the NVIDIA software and hardware stack (CUDA, NCCL)
• Experience with PyTorch
• Familiarity with post-training algorithms such as GRPO, and with Reinforcement Learning

The Public Sector software engineers (SWEs) create the core product building blocks forward-deployed teams use to develop agentic capabilities that function across multiple domains. SWEs responsibilities include building the systems required to ingest and process federal datasets to support real-time decision-making in contested environments. We develop novel agentic enabling capabilities that includes:

• Create multi-layered guardrails around agents
• Optimize data retrieval for agents
• Orchestrate fleets of asynchronous agents
• Automatically alerts users to deviations in data
• Illustrating how an agent reached a decision

As a Senior Software Engineer, you will lead the development of a vertical feature or a horizontal capability to include defining requirements with stakeholders and implementation until it is accepted by the stakeholders.

You will: Lead the design and implementation of scalable backend systems and distributed architectures for Federal customers.

• Manage the full lifecycle of feature development from requirement definition to deployment on classified networks.
• Direct the orchestration of asynchronous agent fleets to meet mission requirements.
• Lead customer engagements to translate mission needs into technical requirements.
• Own the communication with stakeholders to ensure implementation meets defined acceptance criteria.
• Conduct technical reviews and identify risks within machine learning infrastructure and model serving.
• Drive the platform roadmap by providing technical specifications for Federal product offerings.

Ideally you will have: 

• Full Stack Development: Proficiency in front-end, back-end development and infrastructure, including experience with modern web development frameworks, programming languages, and databases
• Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is a plus
• Data Engineering: Knowledge of ETL (Extract, Transform, Load) processes and experience in building data pipelines to integrate and process diverse data sources. Understanding of data modeling, data warehousing, and data governance principles
• AI Application Integration: Familiarity with integrating Large Language Models (LLMs) and building agentic workflows. Understanding of prompt engineering, retrieval-augmented generation (RAG), and agent orchestration is beneficial.
• Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles
• Collaboration and Communication: Excellent interpersonal and communication skills to effectively collaborate with cross-functional teams, stakeholders, and customers. Ability to clearly articulate technical concepts to non-technical audiences and foster a collaborative work environment
• Adaptability and Learning Agility: Willingness to embrace new technologies, learn new skills, and adapt to defining and evolving project requirements. Ability to quickly grasp and apply new concepts and stay up-to-date with emerging trends in software engineering

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human evaluation and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition.

At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.

At the foundation of these products is the Platform Engineering team. As a Staff Software Engineer, you will define and drive both the architectural roadmap and implementation of core platforms and software systems. You will be responsible for providing high-level vision and driving adoption across the engineering org for orchestration, data abstraction, data pipelines, identity & access management, and underlying cloud infrastructure.  You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies.

Impact and Responsibilities:

• Architectural Vision: You will drive the design and implementation of foundational systems, acting as a bridge between high-level business goals and technical goals.
• Cross-Functional Leadership: You will collaborate with cross-functional teams to define and drive adoption of the next generation of features for our AI data infrastructure.
• Technical Ownership: You are responsible for proactively identifying and driving opportunities for organizational growth, driving improvements in programming practices, and upgrading the tools that define our development lifecycle.
• Technical Mentorship: You will serve as a subject matter expert, presenting technical information to stakeholders and providing the guidance to elevate the engineering culture across the company.

Ideally you’d have:

• 8+ years of full-time engineering experience, post-graduation with specialities in back-end systems.
• Extensive experience in software development and a deep understanding of distributed systems and public cloud platforms (AWS preferred).
• Demonstrated a track record of independent ownership and leadership across successful multi-team engineering projects
• Possess excellent communication and collaboration skills, and the ability to translate complex technical concepts to non-technical stakeholders.
• Experience working fluently with standard containerization & deployment technologies like Kubernetes, Terraform, Docker, etc.
• Experience with orchestration platforms, such as Temporal and AWS Step Functions.
• Experience with NoSQL document databases (MongoDB) and structured databases (Postgres).
• Strong knowledge of software engineering best practices and CI/CD tooling (CircleCI, ArgoCD).

Nice to haves:

• Experience with data warehouses (Snowflake, Firebolt) and data pipeline/ETL tools (Dagster, dbt).
• Experience scaling products at hyper-growth startups.
• Excitement to work with AI technologies.

Scale AI’s Public Sector business is growing quickly as government agencies adopt AI to support critical national security, defense, and public sector missions. We’re looking for a hands-on Field Engineering Manager on the front lines leading a team of our field engineering efforts for our federal AI projects, having the opportunity to meaningfully impact millions of dollars in revenue by working closely with our largest public sector customers and ensuring that they become passionate, lifelong Scale customers. This role is ideal for someone who thrives in technical environments, enjoys managing teams while staying close to the code, and wants to work on meaningful problems that impact real world operations across the U.S. government. 

Field Engineers are software engineering generalists who enjoy working with clients directly, taking ambiguous requirements and issues, and quickly designing and building production-grade solutions in close collaboration with the Product, Engineering, and Delivery teams. Field Engineers are experts on Scale's products and codebase, and have general technical knowledge on GenAI systems, APIs, networking, and data engineering. Field Engineers are strong relationship builders, great project managers, and always fun to work with. You’ll play a critical role in delivering technical solutions—all while helping your team grow and execute.

You’ll split your time between technical planning and execution (50%) and people management and team development (50%), leading a team of 6-8 engineers. You’ll work cross-functionally with product, engineering, security, and delivery teams to ensure our engineering efforts meet complex federal compliance, security, and performance needs.

Day to day:

• 50% Field engineering - customer engagement, technical development and delivery 
• 50% Managing a growing team

You will:

• Recruit a high-performing engineering team. 
• Drive engineering productivity. Provide guidance, mentorship, and technical leadership to a team of customer facing engineers working on Generative AI projects. 
• Collaborating with cross-functional teams to define, design, and execute strategic roadmap based on customer needs.
• Work directly with customers to understand their problems and translate those into custom workflows within Scale’s platform.
• Be open to ~25% travel or relocation to a key customer geographic location.
• Collaborate with cross-functional teams to define the vision for backend solutions, ensuring they meet the unique needs of government agencies operating in secure environments.
• Implement end-to-end data integrations, syncing customer’s data to Scale’s platform and back.
• Participate actively in customer engagements, working closely with stakeholders to understand requirements and deliver innovative solutions.
• Lead end-to-end technical delivery of custom workflows from scoping to development to testing to implementation
• This role will require an active TS/SCI security clearance or the ability to obtain a security clearance.

Ideally you’d have:

• 5+ years of full-time engineering experience, post-graduation.
• 2+ years of prior engineering management or equivalent experience and previously managed an engineering team.
• Track record of success as a hybrid customer facing engineer, forward deployed software engineer, and ability to quickly adapt to different roles.
• Prior experience developing with Python and JavaScript, or other modern software languages. 
• Cloud-Native Technologies: Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is a plus.
• Linux experience: Understanding of shell scripting, operating systems, etc.
• Networking experience: Understanding of networking technologies, configuration  (ports, protocols, etc) is a plus.
• Data Engineering: Knowledge of ETL (Extract, Transform, Load) processes and experience in building data pipelines to integrate and process diverse data sources. Understanding of data modeling, data warehousing, and data governance principles.
• Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles.
• Understand unique DoD and USG constraints when it comes to technology.

Scale AI is hiring a highly skilled Senior Software Engineer to help design, build, and operate secure, scalable infrastructure that empowers our employees to do their best work. You’ll join a creative, fast-moving, solutions-oriented group that architects and implements automation across identity and access management, endpoint management, and our broader SaaS stack. The ideal candidate will leverage various in-house and external models/platforms to build applications, slackbots, and dashboards for internal users, and streamline complex workflows through automation. This role will play a major role in elevating the overall velocity of all G&A teams at the organization. 

The Impact You Will Have

• As a full stack engineer (60% BE / 40% FE), you will work with your team to build amazing tools/applications for internal company use as well as external customers/partners
• You will design, develop, test, and support full-stack applications on cloud-native distributed systems
• You will build real-time integrations with SaaS platforms across the organization
• You will build a quality framework/unit tests that ensure product quality, performance, and load and are able to debug/identify system issues
• You will collaborate with the broader teams, engage in engineering council, conduct code reviews, and improve our delivery process
• Collaborate with the IAM team to manage cloud-based identity (Okta) and access controls, ensuring compliance with security policies and standards for internal applications.
• Work heavily cross-functionally to identify areas for corporate technology service improvement and implement self-service solutions.

What We Look For

• 5+ years of related experience with a Bachelor’s degree; or equivalent work experience.
• Proficiency with UI frameworks such as React or Angular and HTML, CSS, Typescript, etc
• Proficiency with backend technologies, including API development, databases, and privacy permissions. Ex. Go, Python, PostgreSQL and GraphQL
• Experience with end-to-end testing and documentation
• Experience developing secure, scalable, and resilient applications on the cloud that handle sensitive data
• Experience with cloud technologies, e.g., AWS, Azure, GCP, Docker, or Kubernetes
• Familiarity with compliance frameworks such as SOC 2, ISO 27001, FedRAMP, and NIST.

Scale is a vital part of bringing AI-enabled technologies to the world, from autonomous driving to drones, robots, and large language models. For example, Scale works with the world's top self-driving car and robotics ML teams as well as the largest companies in the generative AI space. 

As our customer base is growing, you will be on the front lines of our field engineering efforts for our federal AI projects, having the opportunity to meaningfully impact millions of dollars in revenue by working closely with our largest public sector customers and ensuring that they become passionate, lifelong Scale customers.

Our Field Engineers ensure customers' experiences with Scale's technology are seamless and optimized to build a successful, long-term partnership. Field Engineers partner closely with Product, Engineering, and Operations to lead existing customers through building production-grade data pipelines. We do this by knowing our customer’s use-cases inside and out, and creating the tooling and documentation needed to scalably support their growth.

Field Engineers in general are experts on Scale's products and codebase, and have general technical knowledge on web development, using APIs, and working with large volumes of data. Field Engineers are strong relationship builders, great project managers, and always fun to work with. 

The ideal person is a software engineer generalist who enjoys working with clients directly, taking ambiguous requirements and issues, and quickly designing and building sustainable solutions in close collaboration with the Product and Engineering teams. This role is at the intersection of customer delivery, product, and engineering, giving you a breadth of experience and fully leveraging both sides of your brain. If this sounds like you, we invite you to apply!

You will:

• Implement end-to-end data integrations, syncing customer’s data to Scale’s platform and back
• Work closely with our customer’s engineering teams to optimize data pipelines, including providing input towards designing API endpoints and input/output schemas
• Design, develop and maintain playbooks, internal tools, Scale’s documentation and SDKs to quickly get customers set up for long-term success
• Partner with Software Engineers and Operations to remove any technical hurdles customers may face / Debug technical issues impacting delivery and own technical escalations coming from the customer
• Be accountable for the customer’s technical experience throughout their time with Scale
• Work with AEs and PMs to identify customer-specific feature requests
• Develop technical domain expertise in areas of Large Language Models, 2D Image Annotation, and working with API integrations
• Evangelize Scale and interact with customers at major events
• Have an active TS/SCI clearance

Ideally you'd have:

• Track record of success as a hybrid customer-facing engineer or similar function, wearing multiple hats along the way
• Prior technical hands-on experience working with clients in a pre or post-sales capacity to realize business goals
• Prior experience developing with Python and JavaScript, or other modern software languages
• Strong engineering background preferably in Computer Science, Mathematics, or other quantitative fields
• Strong project management and interpersonal skills
• Presentation skills with a high degree of technical credibility when speaking with executives and front-line engineers
• High level of comfort communicating effectively across internal and external organizations
• Intellectual curiosity, empathy, and ability to operate with high velocity

Nice to haves:

• Experience in AI
• Experience working in classified environments
• Previous experience as a technical go-to-market resource
• Understanding of DevSecOps principles, including secure software delivery, infrastructure as code, and continuous integration/deployment
• Hands-on knowledge of container orchestration with Kubernetes and experience with Helm charts for application deployment
• Experience automating infrastructure and configuration management using Ansible or similar tools

We are seeking a highly technical Security Engineer to join our Product Security team. This role is integral to ensuring the security and integrity of our products and services. You will conduct in-depth code reviews, implement security best practices, and influence the overall security strategy. Your expertise in TypeScript, Python, AWS, CI/CD, SAST, DAST, and terraform orchestration will be crucial in identifying and mitigating potential security vulnerabilities. You will also structure complex problems, diagnose root causes independently, and clearly explain the mechanics and significance of security vulnerabilities, including their exploitability and potential impact.

You will:

• Leverage broad product security expertise to build and maintain software tooling that secures every layer of the modern AI/ML software ecosystem.
• Conduct in-depth code reviews to identify and remediate security vulnerabilities.
• Evaluate and enhance the security of our product offerings, through RFC and service review.
• Implement and maintain CI/CD pipelines with a strong focus on security.
• Perform Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST) to identify vulnerabilities in production code.
• Utilize terraform orchestration to ensure secure and efficient infrastructure management.
• Guide engineering teams to build robust long-term solutions that consider security and privacy. 
• Clearly explain the mechanics and significance of security vulnerabilities, including their exploitability and potential impact.
• Influence the security strategy and direction of the team, advocating for best practices and continuous improvement.

Ideally, you’d have:

• Demonstrated ability to drive multi-month security initiatives independently, from problem definition through execution, without requiring significant direction.
• Proven experience as a Security Engineer with a focus on product security.
• Proficiency in NodeJS, TypeScript, Python, and/or Kubernetes.
• Strong understanding of modern Javascript application design.
• Production experience operating and securing AWS infrastructure at scale.
• Hands-on experience with SAST and DAST tools and methodologies.
• Familiarity with terraform orchestration for infrastructure management.
• You can structure complex problems and diagnose root causes independently, providing actionable insights without requiring manager input.
• Excellent communication skills, with the ability to clearly present technical concepts and their implications to both technical and non-technical stakeholders.
• Demonstrated ability to influence security strategies and drive improvements within a team.
• Relevant security certifications (e.g., CISSP, CEH, OSCP) are a plus.

About Scale AI

Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities.

Role Overview

As a Forward Deployed AI Engineer on our Enterprise team, you'll be the technical bridge between Scale AI's cutting-edge AI capabilities and our most strategic customers. You'll work with enterprise clients to understand their unique challenges, architect custom AI solutions, and ensure successful deployment and adoption of AI systems in production environments.

This is a hands-on technical role that combines deep engineering expertise with customer-facing problem solving. You'll work directly with customer engineering teams to integrate AI into their critical workflows.

Key Responsibilities

Customer Integration & Deployment

• Partner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements
• Design and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs)
• Build robust data connectors and ETL pipelines to ingest, process, and prepare customer data for AI workflows
• Deploy and configure AI models and agents within customer security and compliance boundaries

AI Agent Development

• Develop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation
• Architect multi-agent systems that orchestrate between different models, tools, and data sources
• Implement evaluation frameworks to measure agent performance and iterate toward business objectives
• Design human-in-the-loop workflows and feedback mechanisms for continuous agent improvement

Prompt Engineering & Optimization

• Create sophisticated prompt engineering strategies optimized for customer-specific domains and data
• Build and maintain prompt libraries, templates, and best practices for customer use cases
• Conduct systematic prompt experimentation and A/B testing to improve model outputs
• Implement RAG (Retrieval Augmented Generation) systems and fine-tuning pipelines where appropriate

Technical Leadership & Collaboration

• Serve as the primary technical point of contact for strategic enterprise accounts
• Collaborate with customer data scientists, ML engineers, and software developers to ensure smooth integration
• Provide technical training and knowledge transfer to customer teams
• Work closely with Scale's product and engineering teams to translate customer needs into product improvements
• Document technical architectures, integration patterns, and best practices

Problem Solving & Innovation

• Debug complex technical issues across the entire stack, from data pipelines to model outputs
• Rapidly prototype solutions to unblock customers and prove out new use cases
• Stay current on the latest AI/ML research and tools, bringing innovative approaches to customer problems
• Identify opportunities for productization based on common customer patterns

Required Qualifications

• 4+ years of software engineering experience with strong fundamentals in data structures, algorithms, and system design
• Production Python expertise with experience in modern ML/AI frameworks (e.g., LangChain, LlamaIndex, HuggingFace, OpenAI API)
• Experience with cloud platforms (AWS, GCP, or Azure) and modern data infrastructure
• Strong problem-solving skills with the ability to navigate ambiguous requirements and rapidly iterate toward solutions
• Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical audiences

Preferred Qualifications

Agent Development Wiz

• Deep understanding of LLMs including prompting techniques, embeddings, and RAG architectures
• Experience building and deploying AI agents or autonomous systems in production
• Knowledge of vector databases and semantic search systems
• Contributions to open-source AI/ML projects

Infrastructure Guru

• Experience with containerization (Docker, Kubernetes) and CI/CD pipelines
• Experience using Terraform, Bicep, or other Infrastructure as Code (IaC) tools
• Previous work in a devops, platform, or infra role
• Familiarity with enterprise security, compliance, and governance requirements (SOC 2, GDPR, HIPAA)

Customer Product Whisperer

• Proven ability to work with customers in a technical consulting, solutions engineering, or product engineering role
• Domain expertise in verticals like finance, healthcare, government, or manufacturing
• Experience with technical enablement or teaching programs

Sample Projects

The following are some examples of the types of projects we’ve worked on with customers. All of these projects leverage customer data, integrate directly into customers’ existing systems, and are deployed on their infrastructure.

Deep Research for Due Diligence

For a global professional services firm, we developed a sophisticated deep research agent to assist in due diligence. This agent employs a multi-agent architecture for robust fact-checking, integrates several internal MCP tools, and processes complex, unstructured data sources. This solution reliably saves employees hundreds of hours weekly.

Churn Prediction

Working with a TelCo organization, we built a model utilizing customer data to predict churn likelihood. The system then curates personalized offers based on this prediction. This model was integrated into a "next best action" copilot, enabling call center agents to proactively surface relevant offers to customers, leading to a significant reduction in churn.

Data Extraction Voice Agent

We partnered with a healthcare organization to create a lifelike voice agent and avatar designed to gather unstructured health information from patients. Engineered for low latency, the agent adeptly manages conversational flow, adheres to safety guardrails, and efficiently handles data extraction. This automation saves the organization's nurses hundreds of hours each week.

About Scale AI

Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities.

Role Overview

As a Forward Deployed AI Engineering Manager on our Enterprise team, you'll be the technical bridge between Scale AI's cutting-edge AI capabilities and our most strategic customers. You'll work with enterprise clients to understand their unique challenges, lead a team that architects specific AI solutions, and ensure successful deployment and adoption of AI systems in production environments.

This is a Management role that combines deep engineering and AI expertise, leading a team, and working on customer-facing problems. You'll work directly with customer engineering teams to integrate AI into their critical workflows.

Key Responsibilities

Customer Integration & Deployment

• Partner directly with enterprise customers to understand their technical infrastructure, data pipelines, and business requirements
• Design and implement custom integrations between Scale AI's platform and customer data environments (cloud platforms, data warehouses, internal APIs)
• Build robust data connectors and ETL pipelines to ingest, process, and prepare customer data for AI workflows
• Deploy and configure AI models and agents within customer security and compliance boundaries

AI Agent Development

• Develop production-grade AI agents tailored to customer use cases across domains like customer support, data analysis, content generation, and workflow automation
• Architect multi-agent systems that orchestrate between different models, tools, and data sources
• Implement evaluation frameworks to measure agent performance and iterate toward business objectives
• Design human-in-the-loop workflows and feedback mechanisms for continuous agent improvement

Prompt Engineering & Optimization

• Create sophisticated prompt engineering strategies optimized for customer-specific domains and data
• Build and maintain prompt libraries, templates, and best practices for customer use cases
• Conduct systematic prompt experimentation and A/B testing to improve model outputs
• Implement RAG (Retrieval Augmented Generation) systems and fine-tuning pipelines where appropriate

Leadership & Collaboration

• Serve as the Engineering Manager and technical point of contact for strategic enterprise accounts
• Lead a team that is collaborating with customer data scientists, ML engineers, and software developers to ensure smooth integration
• Work closely with Scale's product and engineering teams to translate customer needs into product improvements
• Document technical architectures, integration patterns, and best practices

Problem Solving & Innovation

• Debug complex technical issues across the entire stack, from data pipelines to model outputs
• Rapidly prototype solutions to unblock customers and prove out new use cases
• Stay current on the latest AI/ML research and tools, bringing innovative approaches to customer problems
• Identify opportunities for productization based on common customer patterns

Required Qualifications

• 5+ years of software engineering experience with 2+ yrs of  Management experience with strong fundamentals in data structures, algorithms, and system design
• Production Python expertise with experience in modern ML/AI frameworks (e.g., LangChain, LlamaIndex, HuggingFace, OpenAI API)
• Experience with cloud platforms (AWS, GCP, or Azure) and modern data infrastructure
• Strong problem-solving skills with the ability to navigate ambiguous requirements and rapidly iterate toward solutions
• Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical audiences

Preferred Qualifications

Agent Development Wiz

• Deep understanding of LLMs including prompting techniques, embeddings, and RAG architectures
• Experience building and deploying AI agents or autonomous systems in production
• Knowledge of vector databases and semantic search systems
• Contributions to open-source AI/ML projects

Infrastructure Guru

• Experience with containerization (Docker, Kubernetes) and CI/CD pipelines
• Experience using Terraform, Bicep, or other Infrastructure as Code (IaC) tools
• Previous work in a devops, platform, or infra role
• Familiarity with enterprise security, compliance, and governance requirements (SOC 2, GDPR, HIPAA)

Customer Product Whisperer

• Proven ability to work with customers in a technical consulting, solutions engineering, or product engineering role
• Domain expertise in verticals like finance, healthcare, government, or manufacturing
• Experience with technical enablement or teaching programs

Sample Projects

The following are some examples of the types of projects we’ve worked on with customers. All of these projects leverage customer data, integrate directly into customers’ existing systems, and are deployed on their infrastructure.

Deep Research for Due Diligence

For a global professional services firm, we developed a sophisticated deep research agent to assist in due diligence. This agent employs a multi-agent architecture for robust fact-checking, integrates several internal MCP tools, and processes complex, unstructured data sources. This solution reliably saves employees hundreds of hours weekly.

Churn Prediction

Working with a TelCo organization, we built a model utilizing customer data to predict churn likelihood. The system then curates personalized offers based on this prediction. This model was integrated into a "next best action" copilot, enabling call center agents to proactively surface relevant offers to customers, leading to a significant reduction in churn.

Data Extraction Voice Agent

We partnered with a healthcare organization to create a lifelike voice agent and avatar designed to gather unstructured health information from patients. Engineered for low latency, the agent adeptly manages conversational flow, adheres to safety guardrails, and efficiently handles data extraction. This automation saves the organization's nurses hundreds of hours each week.

Scale AI is seeking a highly skilled and motivated Software Engineer, ARC (Architecture, Reliability, & Compute) to join our dynamic Public Sector Engineering team. As a part of this team, you will define how the company ships software, establishing the patterns for deploying into complex government and high-security environments, rather than just running Terraform scripts. You will build and maintain internal CLIs/tools that standardize testing, deployment, environment management and are tools that engineering relies on to prevent downstream breakages. You will execute on automated deployment efforts to pay down tech debt, creating fully functional staging/testing environments, and defining the company's standard for safe deployments.

You will:

• Design and implement secure scalable backend systems for Public Sector customers, leveraging Scale's modern and cloud-native AI infrastructure.
• Own services or systems and define their long-term health goals, while also improving the health of surrounding components.
• Re-architect the stack to run in compliant or restrictive environments. This requires designing swappable components (auth, storage, logging) to meet government/security mandates without breaking the product.
• Collaborate with cross-functional teams to define and execute the vision for backend solutions, ensuring they meet the unique needs of government agencies operating in secure environments.
• Participate actively in customer engagements, working closely with stakeholders to understand requirements and deliver innovative solutions.
• Contribute to the platform roadmap and product strategy for Scale AI's Public Sector business, playing a key role in shaping the future direction of our offerings.

Must have:

• At least an active secret clearance and the ability & willingness to up level to TS/SCI with CI Poly. This is a requirement and candidates will not be considered who do not hold at least a secret clearance

Ideally you'd have:

• Full Stack Development: Proficiency in both front-end and back-end development, including experience with modern web development frameworks, programming languages, and databases. Experience with developing & delivering software to air-gapped & isolated environments is a plus.
• Cloud-Native Technologies: Understanding of containerization (e.g., Docker) and container orchestration (e.g., Kubernetes) is desired. Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and experience in developing and deploying applications in a cloud-native environment. 
• Security Focused: Experience with Federal Compliance frameworks, and requirements(e.g, Cloud SRG, FedRAMP, STIG Benchmarks, etc). Experience developing software & technical solutions that meet strict security & regulatory compliance requirements.
• Problem Solving: Strong analytical and problem-solving skills to understand complex challenges and devise effective solutions. Ability to think critically, identify root causes, and propose innovative approaches to overcome technical obstacles.
• Collaboration and Communication: Excellent interpersonal and communication skills to effectively collaborate with cross-functional teams, stakeholders, and customers. Ability to clearly articulate technical concepts to non-technical audiences and foster a collaborative work environment.
• Adaptability and Learning Agility: Willingness to embrace new technologies, learn new skills, and adapt to evolving project requirements. Ability to quickly grasp and apply new concepts and stay up-to-date with emerging trends in software engineering.
• Must be able to support work 3-4 days a week from the DC, SF, NYC, or STL office.

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human evaluation and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition.

At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.

At the foundation of these products is the Platform Engineering team. In this role, you will lead the design and development of core data storage, streaming, caching, and indexing platforms and underlying systems.  You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies.

You will:

• Drive the architecture, design, implementation, and reliability of our foundational data platforms and systems, working closely with stakeholders and internal customers to understand and refine requirements.
• Collaborate with cross-functional teams to define, design, and deliver new features.
• Proactively identify opportunities for, and driving improvements to, current programming practices, including process enhancements and tool upgrades.
• Present technical information to teams and stakeholders, providing guidance and insight on development processes and technologies.
• Provide technical leadership, including: upholding and upleveling engineering standards across the organization, mentoring junior engineers. 

Ideally you’d have:

• 8+ years of full-time engineering experience, post-graduation with specialties in back-end systems, specifically related to building large-scale data storage, streaming, and warehousing systems.
• Extensive experience in various database technologies (MongoDB, Postgres), streaming/processing solutions (Kinesis, Flink, Spark), indexing/caching (ElasticSearch, Redis), and various data query engines (Trino, Presto, Snowflake, etc.).
• Show a track record of mentoring and leading teams in successful projects.
• Possess excellent communication and collaboration skills, and the ability to translate complex technical concepts to non-technical stakeholders.
• Experience working fluently with standard containerization & deployment technologies like Kubernetes and various public cloud offerings.
• Extensive experience in software development and a deep understanding of distributed systems, cloud platforms and data systems.
• Experience driving cross functional collaboration and communication at an organizational or broader level. 

Nice to haves:

• Strong knowledge of software engineering best practices and CI/CD tooling (CircleCI).
• Experience with performance tuning and cost optimizations of cloud based data platforms. 
• Experience defining a data lifecycle strategy and designing/implementing tooling for data privacy (i.e. GDPR) needs.  
• Experience scaling products at hyper-growth startups.
• Excitement to work with AI technologies.

Scale GP (Scale Generative AI Platform) is an enterprise-grade Generative AI platform providing APIs for knowledge retrieval, inference, evaluation, and more. We are seeking a strong Senior Full-Stack Engineer to help us build, scale, and refine our rapidly growing product. The ideal candidate is deeply grounded in software engineering best practices and experienced in developing and scaling modern web applications end-to-end. You will work across the stack—from React/TypeScript frontends to Python-based backends—while integrating with LLMs and machine learning systems.

You will solve complex challenges in scalability, reliability, and product experience while owning significant product areas in a fast-paced environment.

What You’ll Do

• Own major full-stack product areas, driving features from design through production deployment.
  
  
• Build modern frontend experiences using React and TypeScript, ensuring performance, usability, and responsiveness.
  
  
• Develop reliable backend services in Python, working with distributed systems, data pipelines, and ML/LLM components.
  
  
• Integrate with LLMs, vector databases, and AI infrastructure to power intelligent product experiences.
  
  
• Deliver experiments and new features quickly, maintaining high quality and tight feedback loops with customers.
  
  
• Collaborate across product, ML, and infrastructure teams to shape the direction of Scale GP.
  
  
• Adapt quickly—learning new technologies, frameworks, and tools as needed across the stack.
  
  

Ideal Experience

• 5+ years of full-time engineering experience, post-graduation.
  
  
• Strong experience developing full-stack applications using React, TypeScript, and Python.
  
  
• Experience scaling or shipping products at high-growth startups.
  
  
• Familiarity with LLMs, vector databases, embeddings, or other modern AI tooling (tinkering or production experience welcome).
  
  
• Proficiency with SQL and modern API development.
  
  
• Experience with Kubernetes, containerization, and microservice architectures.
  
  
• Experience working with at least one major cloud provider (AWS, GCP, or Azure).

About the Team

This role will lead either our Gateway or Agent team, depending on your experience and interests.

Both teams sit on the critical path of every ngrok connection and are central to our reliability, performance, and customer trust.

• Gateway Team: Owns the core infrastructure that handles and shapes all traffic flowing through ngrok—routing, security enforcement, rate limiting, and observability. Every request passes through this layer, and its correctness and performance are foundational to everything we do.
• Agent Team: Builds the software that runs on the customer side of every connection. The Agent is what developers install and trust to securely expose their systems to the internet, and it must perform reliably across unpredictable, real-world environments.

What You’ll Actually Do

Lead a team that ships and learns quickly

You’ll manage and coach a team of 5–7 engineers. The goal isn’t just shipping—it’s building a team that can consistently make good decisions, move fast, and improve over time.

Treat management like a systems problem (with humans in the loop)

You’ll design how your team works: planning, execution, retros, feedback loops, and decision-making. You’ll tune the system, not just react to it.

Stay close enough to the code to matter

You won’t be coding every day, but you’ll get into the details when needed—reviewing designs, debugging tricky issues, and helping the team make better technical tradeoffs.

Drive product and technical direction together

This isn’t “just execution.” You’ll help shape what the team builds—balancing product work, technical investments, and experiments that actually teach you something.

Build a team people want to be on

You’ll coach junior and senior engineers, create psychological safety, and raise the bar on both performance and collaboration.

Connect the team to the bigger picture

You’ll make sure your team understands how their work ties to ngrok’s product, customers, and business. Context isn’t optional here. You’ll work closely with senior leadership and functional orgs to shape and meaningfully impact company culture and roadmap.

You Might Be a Great Fit If…

• You’ve spent 5+ years as a software engineer and comfortable in Go, Rust, Java, or C/C++
• You have 4+ years of engineering leadership experience
• You think in systems—whether that’s distributed systems or team dynamics
• You’re comfortable zooming in (code, debugging) and zooming out (strategy, org design)
• You’ve built things from scratch—teams, processes, or products
• You communicate clearly, directly, and without unnecessary fluff

Bonus points if you’ve:

• Contributed to open source, blogs, or conferences
• Worked in developer tools or product-led growth environments
• Built teams in ambiguous, early-stage contexts

Tech Stack

ngrok runs entirely on AWS. Engineers develop by using remote development tools and/or ssh to connect to remote EC2 environments that run a full Kubernetes cluster of the ngrok stack, closely mirroring production. The codebase is primarily Go and TypeScript. We use Postgres for persistence, Kafka for streaming, Protobuf for service boundaries, and Kubernetes, Terraform, Helm, and Buildkite to operate and ship reliably. React is used for user interfaces, and GitHub supports our development workflows and remembers everything.

Location

This is a remote position for candidates outside of the Bay Area and a hybrid role for candidates within commuting distance to San Francisco. Our Bay Area employees commute to the office on Tuesdays and Wednesdays.

Sponsorship

All candidates must be US-based, and legally authorized to work in the United States.

At this time, ngrok is unable to provide visa sponsorship for this position. Applicants must be authorized to work in the United States on a permanent, ongoing basis without the need for current or future sponsorship.

Compensation

Engineering Manager: $225,000 – $275,000

Job level and actual compensation will be evaluated based on factors including, but not limited to, qualifications objectively assessed during the interview process (including skills and prior relevant experience, potential impact, and scope of role), internal equity with other team members, market data, and specific work location. We provide an attractive mix of salary and equity.

#LI-Remote

About the Role

Anthropic manages one of the largest and fastest-growing accelerator fleets in the industry — spanning multiple accelerator families and clouds. The Accelerator Capacity Engineering (ACE) team is responsible for making sure every chip in that fleet is accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute.

As an engineer on ACE, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend.

You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment.

What This Team Owns

The team’s work spans three functional areas. Depending on your background and interests, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap:

• Data infrastructure — collecting, normalizing, and serving the fleet-wide data that powers everything else. This means building pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalizing billing and usage data across cloud providers, and maintaining the BigQuery layer that the rest of the org queries against. Correctness, completeness, and latency matter here.

• Fleet observability — making the state of the accelerator fleet legible and actionable in real time. This means building cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and driving systemic improvements to scheduling and fragmentation. The work sits at the intersection of Kubernetes operations and cross-team coordination.

• Compute efficiency — measuring and improving how effectively every major workload uses the hardware it’s running on. This means instrumenting utilization metrics across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and collaborating directly with system-owning teams to close efficiency gaps.

• Internal compute tooling — building the platforms and interfaces that make capacity data usable across the org. This includes capacity planning tools, workload attribution systems, cost dashboards, and self-service APIs. The consumers are research engineers, infrastructure teams, finance, and leadership — each with different needs and different levels of technical depth. The work involves product thinking as much as engineering: figuring out what people actually need, defining schema contracts, and making the data discoverable.

You will be placed on a pod based on your background and interests. We are especially focused on hiring for Data Platform, but strong candidates for any of the three active pods will move forward.

What You’ll Do

• Build and operate data pipelines that ingest accelerator occupancy, utilization, and cost data from multiple cloud providers into BigQuery. Own data completeness, latency SLOs, gap detection, and backfill automation.

• Develop and maintain observability infrastructure — Prometheus recording rules, Grafana dashboards, and alerting systems — that surface actionable signals about fleet health, occupancy, and efficiency.

• Instrument and analyze compute efficiency metrics across training, inference, and eval workloads. Build benchmarking infrastructure, establish per-config baselines, and work with system-owning teams to improve utilization.

• Build internal tooling and platforms that enable capacity planning, workload attribution, and cluster debugging. The consumers are other engineering teams, finance, and leadership — not external users.

• Operate Kubernetes-native systems at scale — deploying data collection agents, managing workload labeling infrastructure, and understanding how taints, reservations, and scheduling affect capacity.

• Normalize and reconcile data across heterogeneous sources — including AWS, GCP, and Azure billing exports, vendor-specific telemetry formats, and internal systems with different schemas and billing arrangements.

• Collaborate across organizational boundaries with research engineering, infrastructure, inference, and finance teams. Gather requirements from technical stakeholders, translate them into useful systems, and communicate trade-offs to non-technical audiences.

You May Be a Good Fit If You Have

• 5+ years of software engineering experience with a strong track record building and operating production systems. You write code every day — this is a hands-on engineering role, not a planning or coordination role.

• Kubernetes fluency at operational depth — you’ve operated production K8s at meaningful scale, not just written manifests. Comfort with scheduling, taints, labels, node management, and debugging cluster-level issues.

• Data pipeline engineering experience — designing, building, and owning the full lifecycle of production data pipelines. Experience with data warehouses (BigQuery preferred), schema management, streaming ingestion, SLOs for latency and completeness, and a strong instinct for correctness.

• Observability tooling experience — Prometheus, PromQL, and Grafana are in the critical path for this team. Experience writing recording rules, understanding metric semantics, and building monitoring systems that engineering teams actually rely on.

• Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable.

• Familiarity with at least one major cloud provider (AWS, GCP, or Azure) at the infrastructure level — compute, billing, usage APIs, cost management tooling. Multi-cloud experience is a strong plus.

• High autonomy and strong cross-team communication. You can gather your own requirements, navigate ambiguity, and work across organizational boundaries. Scrappiness and ownership matter more than polish.

Strong Candidates May Also Have

• Multi-cloud data ingestion experience — especially working with AWS and GCP APIs, billing exports, or vendor-specific telemetry formats. Experience normalizing data from external providers with different billing arrangements is directly applicable.

• Accelerator infrastructure familiarity — GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience working with ML training/inference systems at the hardware level.

• Performance engineering and benchmarking experience — building benchmark harnesses, establishing baselines, reasoning about compute efficiency (FLOPs utilization, memory bandwidth, interconnect throughput), and working with system teams to diagnose and improve performance.

• Data-as-product thinking — experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just building pipelines, but thinking about how platform data gets consumed.

• Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or large-scale ML environment. FinOps, chargeback systems, or infrastructure cost modeling.

Familiarity with ClickHouse, Terraform, or Rust. ClickHouse is the team’s current streaming store; Terraform for infrastructure-as-code; Rust for high-performance data collection agents.

The opportunity
At Unity, we're building a world-class advertising platform that operates at a massive global scale. The Ads team powers the systems that connect advertisers with creators and players, driving performance, reliability, and growth across our ecosystem.

As a Staff Software Engineer on the Feature Platform team, you'll play a critical role in building the infrastructure that powers machine learning, experimentation, and optimization across our ads ecosystem. You'll design and operate the systems that transform high-volume event data into production-grade feature datasets used for bidding, attribution, and ranking — working at the intersection of distributed systems, platform engineering, and ML infrastructure.

This role goes beyond data processing. You'll own the full software lifecycle of pipeline systems — from architecture and implementation through to reliability, observability, and performance at scale — collaborating closely with ML, backend, and product teams to build durable, high-quality infrastructure that supports both offline training workflows and online feature serving.

This role is ideal for someone who enjoys owning complex systems end-to-end, thinking deeply about correctness and scalability, and building software that other engineering teams depend on.

What you'll be doing

• What You'll Be Doing
• Design, build, and operate scalable, production-grade data pipeline systems and curated feature datasets powering ads optimization and ML
• Own end-to-end offline data flows from raw event ingestion to feature-ready datasets, with strong emphasis on correctness, reproducibility, and SLA compliance
• Develop and maintain large-scale batch and streaming systems (Python / Java / SQL) with a strong focus on performance, cost-efficiency, and reliability
• Build and contribute to our Feature Store platform, including integration with the high-throughput online serving layer (Go-based services)
• Translate complex product and monetization logic into well-engineered, extensible systems serving analytics and machine learning use cases
• Drive improvements in observability, testing frameworks, and quality standards across the platform
• Lead architectural decisions and engineering best practices within the Feature Platform team

What we're looking for

• Strong software engineering fundamentals with deep experience designing and operating large-scale distributed systems in production
• Hands-on experience building production-grade ETL/ELT pipelines using Python, Java, SQL, or similar technologies
• Experience with distributed processing frameworks such as Spark or Flink in both batch and streaming modes, including performance tuning and parallel computation
• Understanding of how offline data systems integrate with online serving layers — feature stores, low-latency APIs, and real-time systems
• Experience with cloud-native environments, containerized systems, Kubernetes, and workflow orchestration tools
• Strong ownership mindset — focused on correctness, observability, and long-term maintainability
• Experience with Go is a plus, particularly for collaboration on high-throughput feature serving services

You might also have

• Experience with ML infrastructure, feature stores, or model training pipelines
• Background in ads, attribution, or monetization systems
• Familiarity with experimentation and metrics infrastructure
• Exposure to high-scale backend or platform engineering

Additional information

• Relocation support is not available for this position

Benefits
At Unity, we want our team members to thrive. We offer a wide range of benefits designed to support well-being and work-life balance.

Please note: Benefits eligibility, specific offerings, and coverage vary based on the country and employment status.

While specific benefits vary, here are some of the ways we strive to take care of our eligible team members globally: Comprehensive health, life, and disability insurance | Commute subsidy | Employee stock ownership | Competitive retirement/pension plans | Generous vacation and personal days | Support for new parents through leave and family-care programs | Office food snacks | Mental Health and Wellbeing programs and support | Employee Resource Groups | Global Employee Assistance Program | Training and development programs | Volunteering and donation matching program

Life at Unity
Unity [NYSE: U] is the world’s leading game engine, powering play for more than 3 billion consumers each month. The top mobile games in the world, the most played PC indie titles, the most innovative console games, and virtually all of the top XR and Web Games are developed, deployed, and grown in Unity. Unity also enables teams across industries like automotive, manufacturing, and healthcare to design, simulate, and collaborate in 3D — closing the gap between ideas and reality. For more information, please visit www.unity.com.

Unity is a proud equal opportunity employer. We are committed to fostering an inclusive, innovative environment and celebrate our employees across age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, or any other protected status in accordance with applicable law. Our differences are strengths that enable us to support the growing and evolving needs of our customers, partners, and collaborators. If you have a disability that means there are preparations or accommodations we can make to help ensure you have a comfortable and positive interview experience, please fill out this form to let us know.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

This position requires the incumbent to have a sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English.

Headhunters and recruitment agencies may not submit resumes/CVs through this Web site or directly to managers. Unity does not accept unsolicited headhunter and agency resumes. Unity will not pay fees to any third-party agency or company that does not have a signed agreement with Unity.

Your privacy is important to us. Please take a moment to review our Prospect Privacy Policy and Applicant Privacy Policy. Should you have any concerns about your privacy, please contact us at DPO@unity.com.

#DIR #LI-ED1

The role 

The Technical Director of Media and Entertainment, AI Infrastructure is a senior engineering leader who will initially operate as an individual contributor at the intersection of deep AI infrastructure engineering and the business of media and entertainment, with a clear path to building and leading a team as the business scales.

You will work alongside CTOs and engineering leaders at studio groups, VFX houses, gaming studios, agency holding companies, and generative AI model companies. Your role is not to pitch them; it is to build with them. You will translate their most complex infrastructure problems into engineered solutions, own the technical relationship with our most strategic ISV and cloud-native partners, and use what you learn in the field to directly influence the M&E product roadmap alongside Nebius's global Head of Product and Head of Engineering.

This is a ground-floor opportunity to define what AI-native infrastructure looks like for an industry in the middle of a fundamental transformation.

You are welcome to work remotely from the United States (SF Bay Area or NYC preferred) Travel expectations 10%-20% for executive meetings and key industry events. 

Your responsibilities will include: 

AI Infrastructure Architecture

• Own the Technical Blueprint: Personally architect the infrastructure solutions for our most strategic M&E partnerships , studio-scale content production pipelines, agency data consolidation plays, generative AI model deployments. These architectures must be engineered to survive real-world scale, not just pass a POC.
• The Physics to P&L Narrative: Fluently demonstrate to executive stakeholders how infrastructure decisions , data lake locality, storage tiering, inference optimization, directly impact their business model and operability.

Forensic Requirement Gathering

• Deconstruct the Bottleneck: Go beyond the stated problem to find the technical truth. Translate vague business goals (e.g., “We need lower rendering costs”) into precise engineering requirements (e.g., “We need to optimize the inference batch size on L40s to reduce cost-per-token by 30%”).
• Map the Transition: Identify exactly where a customer sits on the curve from legacy service bureau to AI-native tech platform and prescribe the specific infrastructure intervention needed to move them forward.

ISV & Partner Technical Strategy

• Build and Validate the Integration Layer: Identify, engage, and technically validate relationships with the most critical ISVs in the media and entertainment landscape, from rendering and VFX toolchains to generative AI platforms.
• Define the Standard: It is not enough to support these tools. You will define the reference architectures for how they run best on Nebius infrastructure, and work directly with ISV engineering teams to build and publish those standards.
• Decide What’s Worth Doing: In partnership with the GM, evaluate ISV and partner opportunities on their technical merit and strategic leverage , and be equally rigorous about what not to pursue.

Internal Technical Influence

• Shape the M&E Roadmap: Use forensic evidence from the field to prioritize and justify the M&E vertical roadmap. You will work directly with Nebius’s global Head of Product and Head of Engineering to translate partner and customer needs into product direction.
• Lead the M&E Product Summit: Chair a quarterly summit with Core Engineering leadership, using field evidence to drive roadmap decisions and maintain vertical momentum.

We expect you to have: 

• 12+ years of experience in cloud infrastructure, platform engineering, distributed systems, or a closely related technical domain.
• Executive Presence: Capable of commanding a room of engineers and presenting a layered technical roadmap to a C-Suite. You have operated at the top-to-top level , your counterparts are CTOs and VPs of Engineering.
• Builder Mentality: This role begins as a hands-on individual contributor position. You will architect and ship solutions alongside partners and build the assets (reference architectures, integration playbooks, technical frameworks) that make Nebius's M&E infrastructure strategy defensible and scalable. As the business case develops, this role is explicitly designed to grow into a team-building and leadership function, with the expectation that you will recruit, shape, and run that team. You may have come from a startup, run your own company, or operated within a large org in a way that felt like building from zero, and you know how to transition from maker to multiplier.
• Product-Minded: Experience defining a platform strategy, not just executing tickets. You are comfortable telling a customer “No” when a request creates technical debt, and proposing a better alternative.
• Ambiguity Tolerance: You thrive in environments where requirements are evolving. You do not wait for a roadmap; you build it.
• Forensic Mindset: You are not satisfied with surface-level answers. You dig into the kernel, the logs, and the P&L to find the truth.

AI Infrastructure Expertise

• Mastery of the Stack: Expert-level, production-grade knowledge of GPU architectures (H100, L40s), Kubernetes orchestration including Soperator, high-performance and parallel file systems (e.g., Lustre, WEKA), data lake architecture, and networking constraints (InfiniBand/Ethernet).
• Inference Optimization: You understand the nuances of model serving , batch sizes, quantization, KV caching, latency tradeoffs , and can architect solutions for both massive throughput and real-time (sub-50ms) demands.

Domain Context: Media & Entertainment

Industry Fluency: You have operated within the M&E ecosystem and can speak to the infrastructure implications across the following sub-sectors:

• Gaming
• Multimodal Generative Models
• AdTech
• VFX / Content Production

Non-negotiable: Candidates without deep, production-grade working knowledge of GPU infrastructure and inference-based solutioning will not be considered. This is a hard requirement, not a preference.

AI Infrastructure Expertise 

• Mastery of the Stack: You must be an expert in the physics of AI infrastructure. This includes deep, production-grade knowledge of GPU architectures (H100 vs. L40s), Kubernetes orchestration (K8s), high-performance storage parallel file systems (e.g., Lustre, WEKA), and networking constraints (InfiniBand/Ethernet). 
• Inference Optimization: You understand the nuances of model serving—batch sizes, quantization, KV caching, and latency tradeoffs—and can architect solutions for both massive throughput and real-time (sub-50ms) demands. 

It will be an added bonus if you have:

• Hands-on experience with VFX pipelines, studio-scale content production workflows, or the cloud tooling that powers them , render farm management, distributed simulation, asset pipeline tooling. You understand how content gets made, moved, and transformed at scale, and what infrastructure makes that possible.
• Strategic Empathy: The ability to distinguish between customers who need raw bare-metal access and those who need managed endpoints, and the wisdom to prescribe the right path.

Key Employee Benefits:

• Health Insurance: 100% company-paid medical, dental, and vision coverage for employees and families.
• 401(k) Plan: Up to 4% company match with immediate vesting.
• Parental Leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.
• Remote Work Reimbursement: Up to $85/month for mobile and internet.
• Disability & Life Insurance: Company-paid short-term, long-term, and life insurance coverage.

Compensation

We offer competitive salaries, ranging from $295k - $365k OTE (On Target Earnings) + Equity based on your experience.

Who We Are

EDO is the TV outcomes company. Our measurement platform connects convergent TV airings to the ad-driven consumer behaviors most predictive of future sales, helping advertisers, agencies, networks, streamers, and studios understand what works across linear and streaming TV.

We combine immediate engagement signals with decision science, vertical AI, and investment-grade data to help the advertising industry maximize media impact, optimize creative performance, and value every impression. EDO is headquartered in New York City and Los Angeles with office space in San Francisco. For employees residing full-time in NYC, SF, or LA, we have a hybrid work policy of three days in the office and two remote work days.

The Role

EDO is looking for a Senior Platform Engineer to help accelerate our shift toward a platform model. This is a senior IC role on the Infrastructure team focused on paved paths, reusable infrastructure patterns, and self-service workflows that help software, data, and machine learning engineers ship safely and efficiently.

Our environment is AWS-heavy and data-intensive, with heavy use of EC2, RDS, S3, SageMaker, MWAA/Airflow, Snowflake, dbt, Chef, Terraform, Spacelift, and emerging ECS/container patterns. The right person has strong AWS and infrastructure-as-code fundamentals, prior exposure to platform engineering practices, and the pragmatism to modernize existing systems without assuming everything should be rebuilt from scratch.

We approach infrastructure as an internal product: understanding engineers’ needs, prioritizing high-leverage platform capabilities, and iterating on tools and workflows that make teams faster, safer, and more self-sufficient.

What You Will Do

• Build and evolve EDO's internal platform capabilities: paved paths, reusable infrastructure modules, deployment standards, documentation, and self-service workflows.
• Own and improve Terraform and Spacelift-based infrastructure workflows, including module design, reviews, approvals, drift detection, and guardrails.
• Modernize existing AWS infrastructure, including EC2- and Chef-based systems, while helping teams adopt more standardized and repeatable patterns over time.
• Support production data and ML platform infrastructure, including MWAA/Airflow, Snowflake, dbt, SageMaker, S3, RDS, and related AWS services.
• Partner with software, data, and ML engineers as internal customers, turning recurring infrastructure needs into scalable platform capabilities rather than one-off support work.
• Improve deployment, CI/CD, observability, alerting, and incident response practices so teams can operate production systems with greater confidence.
• Expand pragmatic ECS/container deployment patterns where they create value, without treating Kubernetes as the default answer.
• Participate in a lightweight on-call rotation and use operational learnings to reduce toil, improve reliability, and strengthen the platform.

What We Are Looking For

• 6+ years of experience in platform engineering, infrastructure engineering, SRE, DevOps, cloud infrastructure, or similar roles.
• Experience building, operating, or contributing to an internal developer platform, infrastructure platform, developer productivity team, or similar platform-oriented function.
• Strong production experience with AWS, especially EC2, RDS, S3, IAM, networking, load balancing, and related operational patterns.
• Strong hands-on experience with Terraform or similar infrastructure-as-code tooling, including reusable modules and multi-environment patterns.
• Comfort working in a data-heavy environment and supporting technologies such as Airflow/MWAA, Snowflake, dbt, SageMaker, S3, and RDS.
• Product-minded approach to infrastructure: able to understand engineer pain points, prioritize high-impact platform work, document paved paths, and iterate based on adoption and feedback.
• Strong operational judgment, including debugging production systems, improving observability, and building platform patterns that support reliability, security, and cost efficiency.

Bonus Points

• Hands-on experience with Spacelift or similar IaC orchestration platforms such as Terraform Cloud or Atlantis.
• Experience helping an organization adopt platform engineering practices, including paved paths, golden paths, service templates, and developer-facing documentation.
• Experience with Chef, AWS cost management, security/compliance-driven infrastructure, or production ML/data science infrastructure.

Compensation and Benefits

EDO offers a competitive compensation package. Components of compensation include:

• Mid-stage equity and competitive salary
• Flexible Time Off
• Medical, dental and vision coverage: EDO provides full coverage for individual medical plans and partial coverage for dependent or family plans.
• 401(k) plan, FSA, HSA
• Commuter Benefits
• When in an office, employee meals, snacks, and more

In compliance with New York and California Pay Transparency Law, the salary range for this position is $170,000 to $190,000 plus equity. We note the salary information as a general guideline only, as actual compensation may vary from posting. We will consider various factors to determine the offer for this role, including the scope and responsibilities of the position, relevant work experience, key skills, training, and business considerations.

EDO is an Equal Opportunity Employer. We do not discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status, or any legally protected status. All your information will be kept confidential according to EEO guidelines.

WHO WE ARE

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that unifies identity, intelligence, and omnichannel activation into a single platform – powered by trillions of consumer signals and one of the industry’s largest proprietary databases. Our enterprise clients use the Zeta Marketing Platform (ZMP) to personalize experiences at the individual level across every channel. 

THE ROLE

Our Vertical Applications team builds web applications that bring the power of ZMP to specific industries – Healthcare, Political, Retail, and more. Every vertical comes with its own fascinating challenges: unique compliance requirements, distinct data models, and workflows that genuinely matter to the people using them. It’s the kind of work where you get to learn new domains, solve real problems, and see your applications make a tangible impact. 

You’ll own applications end-to-end: partner with product owners to define the problem, architect the backend, integrate with Zeta’s core platform, build the frontend, ship to production, and ensure operational excellence. We value clean, simple architectures, test-first development, and continuous delivery. AI-assisted development with tools like Cursor, Claude Code, Gemini and Codex is embedded in our everyday workflow, and we’re excited about what agentic engineering unlocks for both development velocity and software quality. 

What You’ll Do 

• Own vertical web applications from idea to production – working closely with product owners, designing the architecture, building it, shipping it, and running it. 

• Architect backend services (Python, Java, Ruby, Node.js) with a focus on simplicity and reusability across verticals, so good patterns compound over time. 

• Build polished frontend experiences in React/TypeScript/Ant Design System that connect seamlessly with backend APIs (REST, Thrift). 

• Ship continuously and confidently through CI/CD, with observability, resilience and reliability built in from the start. 

• Use AI-assisted development tools as a natural part of your workflow to move faster while keeping quality high. 

• Weave AI/LLM capabilities into vertical applications, opening up new possibilities for each domain. 

• Collaborate across teams – mentor fellow engineers, lead technical initiatives, and bring clarity to complex decisions for both technical and non-technical audiences. 

What We’re Looking For 

10+ years of software engineering with a history of owning and operating the systems you build. You’re deeply proficient in at least two of Python, Java, Ruby, or Node.js, with solid React/TypeScript skills on the frontend. Continuous delivery, test-driven development, agile and shift-left are things you genuinely practice – not just checkboxes on a process document. 

You’re comfortable building and operating scalable, distributed, high performant systems and navigating multi-tenant, compliant environments. You’re already using AI-assisted coding tools (Cursor, Claude Code, Gemini, Codex, or similar) and you’re curious about pushing those workflows further.  

You’re a strong communicator who enjoys working directly with product owners, proposing architectures, building consensus across teams, and then making it happen. You’re energized by learning new domains and collaborating with people who care about their craft. 

Nice to Have 

• MarTech or AdTech experience (DSPs, bid stream processing, data-intensive platforms). 

• Domain knowledge in Healthcare, Political, or Retail verticals. 

Tech Stack 

Why You’ll Love It Here 

• Interesting problems across diverse verticals – Healthcare, Political, Retail – on a platform processing trillions of consumer signals. You’ll never be bored. 

• AI-native engineering culture – agentic development isn’t a side experiment, it’s how we build. You’ll be at the forefront of how software teams work in the future. 

• Real ownership and autonomy. We trust our engineers to make great decisions and give them the space to do it. 

• Collaborative, low-ego team that genuinely enjoys working together and learning from each other. 
• Public company (NYSE: ZETA) with strong growth, competitive compensation, equity, and comprehensive benefits. 

BENEFITS & PERKS

• Unlimited PTO
• Excellent medical, dental, and vision coverage
• Employee Equity
• Employee Discounts, Virtual Wellness Classes, and Pet Insurance And more!!

SALARY RANGE

The salary range for this role is $200,000 - $220,000, depending on location and experience. 

PEOPLE & CULTURE AT ZETA

Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.  

We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here:  https://zetaglobal.com/blog/a-look-into-zetas-ergs/ 

ZETA IN THE NEWS!

https://zetaglobal.com/press/?cat=press-releases 

#LI-YW1

WHO WE ARE 

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com.

The Opportunity

At Zeta Global, identity is at the core of our marketing cloud. The Identity team builds and operates backend services that power the Zeta Identity Graph - collecting, resolving, and maintaining billions of behavioral, demographic, and transactional signals. These services enable people-based marketing at scale, ensure trust and transparency in how identity data is managed, and provide critical APIs and event streams consumed across Zeta’s platform. Our systems support more than 235 million consumer profiles and process billions of events monthly, requiring services that are highly available, low-latency, and built to scale

We are looking for a Lead Software Engineer who brings strength in both backend distributed systems and data engineering. In this role, you’ll design APIs and event-driven services, scale our systems to handle rapidly growing data volumes, and modernize our platform for resilience, transparency, and performance.

You Will:

• Design and build backend services and APIs for identity resolution, graph transparency, and integrations.
• Develop and optimize event-driven pipelines and workflows processing billions of signals.
• Deploy and operate cloud-native services on EKS/Kubernetes, leveraging Helm for repeatable deployments.
• Ensure reliability, observability, and scalability across services and data pipelines.
• Collaborate across engineering, product, and data teams to deliver end-to-end identity solutions.
• Mentor engineers and influence architecture and roadmap

You Have:

• 5+ years of experience in backend and/or data engineering at scale.
• Strong skills in Python, Java, or Scala, with expertise in APIs, distributed systems, and event-driven design.
• Hands-on experience with Kafka (or similar), Spark/Hive/Flink, and workflow schedulers (Airflow/Luigi).
• Proficiency with Kubernetes (EKS preferred) and deployment practices (Helm, service meshes).
• Solid knowledge of SQL, data modelling, and cloud platforms (AWS).
• Familiarity with columnar / NoSQL databases (Aerospike, Snowflake, HBase, Scylla, Couchbase).
• Strong communication skills and a track record of driving technical direction and mentoring teams.

Bonus Skills:

• Experience with large-scale or multi-tenant distributed systems.
• Exposure to real-time streaming frameworks (Flink, Storm).
• Knowledge of open table formats (Iceberg, Hudi, Delta Lake).
• Background in security, compliance, or privacy related to identity systems.

BENEFITS & PERKS

• Unlimited PTO
• Excellent medical, dental, and vision coverage
• Employee Equity
• Employee Discounts, Virtual Wellness Classes, and Pet Insurance And more!!

SALARY RANGE

The salary range for this role is $150,000 - $200,000, depending on location and experience. 

PEOPLE & CULTURE AT ZETA

Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.  

We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here:  https://zetaglobal.com/blog/a-look-into-zetas-ergs/ 

ZETA IN THE NEWS!

https://zetaglobal.com/press/?cat=press-releases 

#LI-DD1

WHO WE ARE

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that unifies identity, intelligence, and omnichannel activation into a single platform – powered by trillions of consumer signals and one of the industry’s largest proprietary databases. Our enterprise clients use the Zeta Marketing Platform (ZMP) to personalize experiences at the individual level across every channel. 

THE ROLE

Our Vertical Applications team builds web applications that bring the power of ZMP to specific industries – Healthcare, Political, Retail, and more. Every vertical comes with its own fascinating challenges: unique compliance requirements, distinct data models, and workflows that genuinely matter to the people using them. It’s the kind of work where you get to learn new domains, solve real problems, and see your applications make a tangible impact. 

You’ll own applications end-to-end: partner with product owners to define the problem, architect the backend, integrate with Zeta’s core platform, build the frontend, ship to production, and ensure operational excellence. We value clean, simple architectures, test-first development, and continuous delivery. AI-assisted development with tools like Cursor, Claude Code, Gemini and Codex is embedded in our everyday workflow, and we’re excited about what agentic engineering unlocks for both development velocity and software quality. 

What You’ll Do 

• Own vertical web applications from idea to production – working closely with product owners, designing the architecture, building it, shipping it, and running it. 

• Architect backend services (Python, Java, Ruby, Node.js) with a focus on simplicity and reusability across verticals, so good patterns compound over time. 

• Build polished frontend experiences in React/TypeScript/Ant Design System that connect seamlessly with backend APIs (REST, Thrift). 

• Ship continuously and confidently through CI/CD, with observability, resilience and reliability built in from the start. 

• Use AI-assisted development tools as a natural part of your workflow to move faster while keeping quality high. 

• Weave AI/LLM capabilities into vertical applications, opening up new possibilities for each domain. 

• Collaborate across teams – mentor fellow engineers, lead technical initiatives, and bring clarity to complex decisions for both technical and non-technical audiences. 

What We’re Looking For 

10+ years of software engineering with a history of owning and operating the systems you build. You’re deeply proficient in at least two of Python, Java, Ruby, or Node.js, with solid React/TypeScript skills on the frontend. Continuous delivery, test-driven development, agile and shift-left are things you genuinely practice – not just checkboxes on a process document. 

You’re comfortable building and operating scalable, distributed, high performant systems and navigating multi-tenant, compliant environments. You’re already using AI-assisted coding tools (Cursor, Claude Code, Gemini, Codex, or similar) and you’re curious about pushing those workflows further.  

You’re a strong communicator who enjoys working directly with product owners, proposing architectures, building consensus across teams, and then making it happen. You’re energized by learning new domains and collaborating with people who care about their craft. 

Nice to Have 

• MarTech or AdTech experience (DSPs, bid stream processing, data-intensive platforms). 

• Domain knowledge in Healthcare, Political, or Retail verticals. 

Tech Stack 

Why You’ll Love It Here 

• Interesting problems across diverse verticals – Healthcare, Political, Retail – on a platform processing trillions of consumer signals. You’ll never be bored. 

• AI-native engineering culture – agentic development isn’t a side experiment, it’s how we build. You’ll be at the forefront of how software teams work in the future. 

• Real ownership and autonomy. We trust our engineers to make great decisions and give them the space to do it. 

• Collaborative, low-ego team that genuinely enjoys working together and learning from each other. 
• Public company (NYSE: ZETA) with strong growth, competitive compensation, equity, and comprehensive benefits. 

BENEFITS & PERKS

• Unlimited PTO
• Excellent medical, dental, and vision coverage
• Employee Equity
• Employee Discounts, Virtual Wellness Classes, and Pet Insurance And more!!

SALARY RANGE

The salary range for this role is $200,000 - $220,000, depending on location and experience. 

PEOPLE & CULTURE AT ZETA

Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.  

We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here:  https://zetaglobal.com/blog/a-look-into-zetas-ergs/ 

ZETA IN THE NEWS!

https://zetaglobal.com/press/?cat=press-releases 

#LI-YW1

WHO WE ARE 

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com.

Role Overview

Zeta Global is seeking a Lead Software Engineer to build and scale healthcare-focused applications and data systems that power audience intelligence, activation, measurement, and reporting across both HCP and DTC workflows.

This is a hands-on technical leadership role operating at the intersection of distributed systems, data engineering, and healthcare domain constraints. You will design and deliver systems that integrate identity, audience data, and campaign performance while meeting strict requirements for privacy, compliance, and reliability.

The ideal candidate brings strong system design depth, experience building data-intensive platforms, and the ability to lead through architecture and execution in regulated environments.

What You’ll Build

• Full-stack applications supporting HCP and patient (DTC) audience discovery, segmentation, and activation
  • Scalable data pipelines for ingestion, normalization, and enrichment of healthcare datasets
  • Identity resolution and data linkage services across fragmented data sources
  • Reporting, attribution, and measurement systems connecting campaigns to outcomes
  • APIs and services enabling downstream activation, analytics, and partner integrations

Key Responsibilities

• Lead architecture and hands-on development of distributed systems supporting healthcare data workflows
  • Design and implement scalable data pipelines (batch and streaming) for large-scale datasets
  • Build and maintain APIs and services with strong SLAs, observability, and fault tolerance
  • Implement identity resolution logic including matching, deduplication, and graph-based linkage
  • Ensure high data quality through validation, reconciliation, and monitoring frameworks
  • Design systems with strong security controls, including encryption, access control, and auditability
  • Apply privacy-by-design principles across all systems handling PHI/PII
  • Partner with Product and Data teams to translate healthcare requirements into scalable architectures
  • Drive engineering best practices across testing, CI/CD, code quality, and operational excellence
  • Mentor engineers and act as a technical leader across the team

Engineering Expectations

• Strong experience designing distributed systems (microservices, event-driven architectures)
  • Deep understanding of data modeling, storage systems (OLTP, OLAP), and data processing frameworks
  • Experience with streaming and batch processing (e.g., Kafka, Spark, Flink, or similar)
  • Proficiency in backend development (Python, Java, or Ruby) and API design (REST/gRPC)
  • Experience building modern front-end applications (React or similar)
  • Strong experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code
  • Familiarity with containerization and orchestration (Docker, Kubernetes)
  • Experience implementing observability (logging, metrics, tracing) and reliability patterns
  • Strong understanding of performance optimization and scalability tradeoffs

Healthcare & Domain Expectations

• Experience working with HCP data (provider identity, targeting, segmentation)
  • Experience with patient / DTC data workflows and privacy-aware systems
  • Understanding of healthcare data ecosystems (claims, provider, or audience datasets)
  • Familiarity with identity graphs and data onboarding patterns
  • Experience building reporting or attribution systems tied to business outcomes
  • Strong grasp of HIPAA, PHI/PII, and regulated data handling requirements

Qualifications

• 8–10 years of software engineering experience with increasing technical ownership
  • Proven track record of building and scaling data-intensive or distributed systems
  • Experience working in regulated environments (healthcare strongly preferred)
  • Strong system design and architecture skills
  • Ability to lead technically while remaining hands-on in implementation
  • Strong collaboration skills across engineering, product, and data teams

Preferred

• Experience with healthcare data providers or identity ecosystems
  • Background in AdTech, MarTech, or audience/data platforms
  • Experience with privacy-enhancing technologies (tokenization, clean rooms)
  • Exposure to ML/AI-driven data products or analytics systems

BENEFITS & PERKS

• Unlimited PTO
• Excellent medical, dental, and vision coverage
• Employee Equity
• Employee Discounts, Virtual Wellness Classes, and Pet Insurance And more!!

SALARY RANGE

The salary range for this role is $200,000 - $215,000, depending on location and experience. 

PEOPLE & CULTURE AT ZETA

Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.  

We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here:  https://zetaglobal.com/blog/a-look-into-zetas-ergs/ 

ZETA IN THE NEWS!

https://zetaglobal.com/press/?cat=press-releases 

#LI-TS1

WHO WE ARE 

Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com.

The Opportunity

At Zeta Global, identity is at the core of our marketing cloud. The Identity team builds and operates backend services that power the Zeta Identity Graph - collecting, resolving, and maintaining billions of behavioral, demographic, and transactional signals. These services enable people-based marketing at scale, ensure trust and transparency in how identity data is managed, and provide critical APIs and event streams consumed across Zeta’s platform. Our systems support more than 235 million consumer profiles and process billions of events monthly, requiring services that are highly available, low-latency, and built to scale

We are looking for a Lead Software Engineer who brings strength in both backend distributed systems and data engineering. In this role, you’ll design APIs and event-driven services, scale our systems to handle rapidly growing data volumes, and modernize our platform for resilience, transparency, and performance.

You Will:

• Design and build backend services and APIs for identity resolution, graph transparency, and integrations.
• Develop and optimize event-driven pipelines and workflows processing billions of signals.
• Deploy and operate cloud-native services on EKS/Kubernetes, leveraging Helm for repeatable deployments.
• Ensure reliability, observability, and scalability across services and data pipelines.
• Collaborate across engineering, product, and data teams to deliver end-to-end identity solutions.
• Mentor engineers and influence architecture and roadmap

You Have:

• 5+ years of experience in backend and/or data engineering at scale.
• Strong skills in Python, Java, or Scala, with expertise in APIs, distributed systems, and event-driven design.
• Hands-on experience with Kafka (or similar), Spark/Hive/Flink, and workflow schedulers (Airflow/Luigi).
• Proficiency with Kubernetes (EKS preferred) and deployment practices (Helm, service meshes).
• Solid knowledge of SQL, data modelling, and cloud platforms (AWS).
• Familiarity with columnar / NoSQL databases (Aerospike, Snowflake, HBase, Scylla, Couchbase).
• Strong communication skills and a track record of driving technical direction and mentoring teams.

Bonus Skills:

• Experience with large-scale or multi-tenant distributed systems.
• Exposure to real-time streaming frameworks (Flink, Storm).
• Knowledge of open table formats (Iceberg, Hudi, Delta Lake).
• Background in security, compliance, or privacy related to identity systems.

BENEFITS & PERKS

• Unlimited PTO
• Excellent medical, dental, and vision coverage
• Employee Equity
• Employee Discounts, Virtual Wellness Classes, and Pet Insurance And more!!

SALARY RANGE

The salary range for this role is $150,000 - $200,000, depending on location and experience. 

PEOPLE & CULTURE AT ZETA

Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.  

We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work. We provide a forum for employees to celebrate, support and advocate for one another. Learn more about our commitment to diversity, equity and inclusion here:  https://zetaglobal.com/blog/a-look-into-zetas-ergs/ 

ZETA IN THE NEWS!

https://zetaglobal.com/press/?cat=press-releases 

#LI-DD1