About the role

As part of Enterprise Engineering, the Workday Recruiting/Talent Acquisition Engineer sets the bar for technical excellence and innovation across our Talent Acquisition platform—driving configuration strategy, automation, integration optimization, and continuous validation at scale. You'll bring deep Workday Recruiting and ATS functional knowledge, combined with strong engineering capabilities, applying technical rigor to the configuration, data management, and testing of an enterprise-grade recruiting system in a fast-paced, high-volume hiring environment.

Scope includes Workday Recruiting, Advanced Recruiting, Candidate Experience (Career Site), ATS Tech Stack (HiredScore, Paradox, iCIMS integration), Talent Acquisition processes, Onboarding, and recruiting analytics/reporting. You'll build robust, scalable automated solutions and manage the quality lifecycle in our Workday Recruiting ecosystem, ensuring compliance, data integrity, candidate experience excellence, and recruitment efficiency requirements for a rapidly growing company.

For New York Only - The estimated annual salary for this position is between $132,600 - $191,250 annually. Compensation packages are based on factors unique to each candidate, including but not limited to skill set, certifications, and specific geographical location. This role is eligible for health insurance, equity awards, life insurance, disability benefits, parental leave, wellness benefits, and paid time off.

What you'll be doing

• Design and own the end-to-end Workday Recruiting configuration and integration strategy for Recruiting, Advanced Recruiting, Career Site, ATS integrations (HiredScore, Paradox), Onboarding, and Talent Acquisition workflows—balancing technical excellence with speed to delivery and optimal candidate experience.
• Build, maintain, and scale Workday Recruiting configurations, including job requisition templates, candidate pipelines, offer management, background check integrations, recruiting events, interview scheduling, and agency portal functionality; ensure seamless data flow across systems.
• Design and implement comprehensive automated test coverage for recruiting processes using Tricentis Tosca or similar tools; manage test execution and traceability in qTest; integrate results into CI/CD pipelines for continuous quality validation.
• Create reusable configuration frameworks and templates (job req templates, screening questions, candidate evaluation forms, offer letter templates, recruiting business process steps) that standardize processes, reduce configuration time, and improve system reliability.
• Drive API-level and end-to-end integration testing across the Talent Acquisition ecosystem (e.g., requisition creation to hire, candidate sourcing, screening workflows, offer approval, background checks, I-9 verification, onboarding handoff to HCM), including third-party vendor integrations and Workday web services.
• Validate Workday Recruiting Extend apps, custom objects, and advanced configurations to ensure functional correctness, security, accessibility, compliance (OFCCP, EEOC), and performance under high-volume recruiting loads.
• Establish recruiting-specific quality metrics, dashboards, and reporting (including time-to-fill, candidate conversion rates, system uptime, integration success rates, configuration coverage, defect density) that enable data-driven decision-making and continuous improvement.
• Champion candidate experience excellence: partner with Recruiting, Employer Branding, and TA Operations teams to optimize career site performance, mobile responsiveness, application completion rates, and candidate communication workflows.
• Lead technical design reviews and configuration governance for recruiting process changes, partner with Workday Recruiting Consultants, Integration Engineers, and HRIS teams on design patterns and apply configuration best practices to recruiting assets.
• Drive root cause analysis and systemic improvements across recruiting data quality, process bottlenecks, integration failures, and user experience issues; continuously optimize configuration efficiency and recruiting operations effectiveness.
• Collaborate closely with Talent Acquisition, Recruiting Operations, HR Technology, Compliance, DEI, People Analytics, and vendor partners (HiredScore, Paradox, background check providers) to align technical solutions with business priorities, hiring goals, and regulatory requirements.
• Manage Workday Recruiting roadmap alignment with bi-annual releases and feature updates; evaluate new Recruiting functionality, lead pilot programs, and drive adoption of innovations that enhance recruiter productivity and candidate experience.

We're excited if you have

• 6+ years with SaaS/Cloud HR technology and significant hands-on Workday Recruiting experience across requisition management, candidate management, offer management, career site configuration, recruiting analytics, and ATS integrations.
• Deep functional expertise in Talent Acquisition processes, including high-volume recruiting, executive search, agency management, candidate sourcing, interview management, offer workflows, background checks, and onboarding transitions.
• Strong technical skills in Workday Recruiting configuration, Calculated Fields, Workday Studio, or EIB for data management, integration troubleshooting (Web Services, RaaS reports), and understanding of API-based recruiting technology ecosystems.
• Experience with recruiting automation tools such as HiredScore, Paradox (conversational AI), scheduling tools, CRM platforms, and modern ATS integrations.
• Test automation and quality engineering experience with tools like Tricentis Tosca, Selenium, or similar; ability to design and maintain automated regression suites for recruiting workflows.
• Data-driven mindset with experience building recruiting analytics, dashboards (Workday Reports, Prism), and using data to optimize funnel conversion, process efficiency, and candidate experience.
• Understanding of recruiting compliance requirements, including OFCCP, EEOC, GDPR, accessibility standards (WCAG), and ability to configure systems to meet regulatory obligations.
• Strong stakeholder management and communication skills; ability to translate complex technical concepts for recruiting leaders and translate business requirements into technical solutions.
• Experience managing or contributing to enterprise-scale Workday implementations or transformations; familiarity with Agile/Scrum methodologies and DevOps practices.
• Passion for candidate experience and recruiting innovation; stays current on recruiting technology trends, AI/ML applications in talent acquisition, and Workday Recruiting product roadmap.

#LI-SR2

The Team

The Biohub in New York is an independent nonprofit research institute that brings together three powerhouse universities - Columbia University, The Rockefeller University, and Yale University - into a single collaborative technology and discovery engine. Biohub itself supports some of the brightest, boldest engineers, data scientists, and biomedical researchers to investigate the fundamental mechanisms underlying disease and develop new technologies that will lead to actionable diagnostics and effective therapies. We are guided by our values of scholarly excellence; disruptive innovation; hands-on engineering/hacking/building; partnership and collaboration; open communication and respect; inclusiveness; and opportunity for all.

Our Vision                                                                     

• We pursue large scientific challenges that cannot be pursued in conventional environments
• We enable individual investigators to pursue their riskiest and most innovative ideas
• The technologies developed at Biohub facilitate research by scientists and clinicians at our home institutions and beyond

Diversity of thought, ideas, and perspectives are at the heart of Biohub and enable disruptive innovation and scholarly excellence. We are committed to cultivating an organization where all colleagues feel inspired and know their work makes an important contribution.

The Opportunity

The Biohub is seeking an accomplished Scientist  I to lead innovative investigations into spatial architecture of molecular systems and its role in controlling cell state dynamics. In our newly established Synthetic Spatial Omics Laboratory, we advance next-generation spatial multi-omics platforms for understanding how cells process combinatorial signals, designing synthetic biology toolkits, and capturing cellular temporal histories with spatial resolution.

The successful candidate will lead independent research projects integrating spatial multi-omics, synthetic biology, and systems-level analysis to investigate molecular neighborhood reorganization in response to perturbations, engineer programmable cellular functions, and identify the molecular drivers of cell state transitions. This position offers the opportunity to drive both technology development and fundamental discovery in a highly collaborative, interdisciplinary research environment. 

What You'll Do

• Lead the development and application of imaging-based multi-omics technologies (e.g., seqFISH and related approaches) to map subcellular molecular organization and cell state transitions.
• Design and perform perturbation experiments to decode how molecular neighborhoods respond to combinatorial signals.
• Engineer synthetic biology systems and profile their behavior using spatial multi-omics to understand and program cellular functions.
• Lead analysis of high-dimensional imaging and sequencing datasets using computational and machine learning approaches to identify organizational and regulatory principles. 
• Collaborate closely with researchers across genomics, synthetic biology, machine learning, and related disciplines.
• Contribute to and lead preprints, publications, presentations, and open science practices.

What You'll Bring

Essential - 

• PhD in Systems Biology, Molecular Biology, Immunology, Bioengineering or a closely related discipline.
• 3-4+ years of postdoctoral or equivalent research experience, with a strong record of scientific productivity.
• Demonstrated expertise in imaging-based omics and/or sequencing-based single-cell technologies.
• Expertise in molecular biology techniques, including cell culture, nucleic acid workflows, and cellular functional assays.
• Strong computational skills in Python, R, or comparable languages, with experience analyzing large-scale imaging and sequencing datasets.
• Proven ability to independently conceptualize research directions, design rigorous experiments, troubleshoot complex workflows, and lead projects from conception to publication.
• Collaborative mindset and strong scientific communication skills.

Nice to have - 

• Established track record in developing novel experimental or computational technologies.
• Prior work with imaging-based transcriptomics or genomics technologies.
• Prior work with immune cell types or immune cell engineering.
• Expertise in synthetic biology, CRISPR technologies, or engineered cell systems.
• Experience with temporal genomics, molecular recording, or lineage-tracing approaches.
• Background in regulatory network analysis, signaling dynamics, or models of cellular computation.

Compensation

The New York City, NY base pay range for a new hire in this role is $120,000.00 - $150,000.00. New hires are typically hired into the lower portion of the range, enabling employee growth in the range over time. Actual placement in range is based on job-related skills and experience, as evaluated throughout the interview process. 

Benefits for the Whole You 

We’re thankful to have an incredible team behind our work. To honor their commitment, we offer a wide range of benefits to support the people who make all we do possible. 

• Provides a generous employer match on employee 401(k) contributions to support planning for the future.
• Paid time off to volunteer at an organization of your choice. 
• Funding for select family-forming benefits. 
• Relocation support for employees who need assistance moving

If you’re interested in a role but your previous experience doesn’t perfectly align with each qualification in the job description, we still encourage you to apply as you may be the perfect fit for this or another role.

#LI-Hybrid #LI-Onsite

Deadline to apply: None. Applications will be reviewed on a rolling basis. 

About the team 

The Capacity & Efficiency team sits inside Anthropic’s Compute organization and owns the cost, utilization, and attribution story for non-accelerator infrastructure — the network, compute, and storage backbone that moves petabytes between training clusters, inference fleets, and object storage across clouds and regions. The scale is real, the spend is large, and the efficiency levers are still mostly unpulled.

We work alongside the Systems Networking team (who build and operate the fabric) and the Observability team (who own the telemetry platform). This role lives at the intersection: you’ll use deep networking knowledge and rigorous measurement to figure out where and how bandwidth, latency, and dollars are being used, find optimization opportunities and land them.

About the role

We’re looking for a network engineer who thinks in metrics first. You understand spine-leaf fabrics, BGP, SDN overlays, and cloud interconnect products well enough to build them. You will instrument them, model their cost-per-bit, and squeeze out the inefficiency, while ensuring we can move the bits to the right places in the most efficient manner. You’ll own the observability and efficiency surface for Anthropic’s network: from per-flow telemetry on backbone routers, to cost attribution that tells a research team exactly what their checkpoint sync is costing.

This is a hands-on IC role. You’ll write code (Python, Go), build dashboards and model capacity. You’ll also influence architecture: when the data says a traffic pattern is pathological, you’ll be in the room root causing it and fixing it. 

You will be working across three areas: network telemetry, observability and cost modeling and attribution. We expect you to be strong in at least two and willing to grow into the third. If you're a telemetry-first engineer who's never built a chargeback model, or a traffic engineer who hasn't shipped eBPF probes, apply anyway and tell us which axis you want to grow on.

What you’ll do

• Build the network observability stack. Design and deploy telemetry pipelines — sFlow/IPFIX, gNMI streaming, eBPF host probes — that turn packet counters into per-flow, per-tenant, per-workload cost and utilization data. Own the SLIs for backbone and DCN fabric health.

• Hunt for efficiency. Analyze inter-region traffic patterns, identify hot links and stranded capacity, and quantify the dollar impact. Build the models that tell us whether we should buy more capacity, or move the workload.

• Own QoS and traffic engineering. Design and operate traffic classification, marking, and shaping across the backbone. Make sure bulk checkpoint transfers don’t starve latency-sensitive inference, and that we’re not paying premium cross-region rates for traffic that could take the cheap path.

• Drive cost attribution. Tie network spend — egress, interconnect ports, transit, optical leases — back to the teams and workloads that generate it. Make network cost a first-class input to capacity planning and workload placement decisions.

• Influence decisions you don't own. A large fraction of this role is convincing other teams to act on what your data shows: making the case to research that a traffic pattern needs to change, to finance that an interconnect tranche is worth buying, to Systems Networking that a QoS policy needs rewriting. You'll partner closely with Systems Networking on fabric architecture and Observability on telemetry platform integration, but the cost and efficiency wins will come from moving teams that don't report to you.

• Automate. Extend our intent-based network configuration systems and write the tooling that turns your efficiency findings into safe, reviewable, and impactful changes.

You may be a good fit if you

• Have 5+ years operating large-scale production networks — data center fabrics (spine-leaf, Clos), backbone/WAN, or hyperscaler-adjacent environments. 

• Are genuinely fluent across the stack: BGP (including policy and communities), ECMP, VXLAN/EVPN or equivalent overlays, QoS (DSCP, queuing, shaping), and L1/optical basics (DWDM, coherent, LAGs).

• Know at least one major CSP’s networking model deeply — AWS (VPC, TGW, Direct Connect, Gateway Load Balancer) or GCP (Shared VPC, Interconnect, Cloud Router, Network Connectivity Center) — and understand how their overlays interact with physical underlays.

• Have built or operated network telemetry at scale: streaming telemetry (gNMI/OpenConfig), flow export (sFlow, IPFIX, NetFlow), or eBPF-based host-side instrumentation. You can reason about sampling, cardinality, and storage tradeoffs.

• Comfortable writing Python or Go to build tooling, telemetry pipelines, infrastructure-as-code, config management for network devices and automation, that you’ll ship to production.

• Think quantitatively by default. You reach for a notebook or a Grafana query before you reach for an opinion, and you can turn messy counter data into a defensible cost model.

• Communicate crisply. You can explain to a finance partner why a 10% egress reduction matters, and to a network engineer why a specific ECMP imbalance is costing real money.

Strong candidates may also have

• SRE experience for large-scale network infrastructure — designing for reliability, defining SLOs/SLIs for network services, capacity planning with error budgets, and incident response for network-impacting outages at scale.

• Background on a cloud provider's networking team or a cloud networking product team — building or operating the interconnect, backbone, or SDN control plane from the provider side, not just consuming it as a customer.

• Familiarity with AI/ML infrastructure traffic patterns like collective communication (all-reduce, all-gather), checkpoint/weight transfer, inference serving, and how these stress networks differ than traditional workloads in terms of burst behavior, flow synchronization, and bandwidth symmetry.

• Experience with HPC fabrics like InfiniBand, RoCE v2, lossless Ethernet, or custom high-radix topologies and an understanding of how job placement, congestion management, and adaptive routing interact at scale.

• Background in traffic engineering for large backbones and the operational judgment to know when TE is worth the complexity.

• Hands-on time with multi-cloud connectivity: cross-cloud peering, private interconnect products, and the billing models that come with them.

• Experience building cost/chargeback systems for shared infrastructure, or FinOps exposure in a large cloud environment.

Representative projects

• Build a per-flow cost attribution pipeline that traces every byte of cross-region egress back to the team and workload that generated it

• Design QoS policy for the private backbone that prevents bulk checkpoint transfers from starving inference traffic

• Model whether it's cheaper to buy an additional 1.6Tb interconnect tranche or to re-route traffic through existing capacity

• Instrument DCN fabric utilization with streaming telemetry and build the Grafana dashboards that become the team's source of truth for network observability 

Why this role, why now

Anthropic’s network footprint is growing faster than our ability to reason about it. We’re turning up tens of terabits of private backbone capacity, peering across clouds, and moving model weights that keep getting larger. The efficiency opportunities are enormous and largely untouched — this is a chance to build the measurement and optimization layer from the ground up, with real budget impact and direct influence on how Anthropic’s infrastructure scales.

About the role

The next generation of behavioral health will be built on agentic AI — systems that don't just predict or summarize, but act: scheduling, coordinating care, drafting clinical documentation, surfacing risk, and orchestrating the hundreds of micro-decisions that today consume clinician and operator time.

Charlie Health's ambition is to build the most advanced agentic systems in healthcare. We are investing in a multi-agent architecture grounded in a real-time clinical signal graph, governed by HIPAA-native infrastructure, and deployed against measurable patient outcomes in a national virtual IOP.

We are hiring a Senior Principal AI Engineer to lead the design, development, and deployment of these systems across our platform. This is a hands-on technical leadership role with deep cross-functional reach into Engineering, Product, Data, Clinical, and executive leadership.

Responsibilities

• Serve as the technical owner of agentic AI at Charlie Health, from architecture and model strategy through production deployment and ongoing reliability.
• Lead the development of clinical operations agents that handle scheduling, intake, insurance verification, referral triage, and care coordination, replacing brittle human handoffs with auditable, supervised AI workflows.
• Lead the development of clinician copilots for real-time documentation, risk surfacing, and treatment-plan support, integrated with our EHR and clinical data platform.
• Define and build the agentic substrate: eval frameworks, observability, guardrails, tool registries, prompt and policy versioning, PHI-safe logging, and simulation environments.
• Own the model strategy across frontier models, fine-tunes, distilled specialists, and classic ML, and articulate it clearly to executive and clinical leadership.
• Partner with Product, Clinical, and Data teams to identify high-leverage opportunities for AI across patient and operational experiences.
• Establish best practices around AI reliability, safety, evaluation, and explainability in a regulated clinical environment.
• Mentor engineers and data scientists across the organization and help foster a strong culture of technical excellence and responsible AI innovation.

What you'll work with

• An executive team that has committed to an AI-native operating model and is funding it.
• An AI Guild of senior engineers, data scientists, and clinical operators piloting AI across the company today.
• A modern data foundation including Snowflake, dbt, pgvector, and an emerging Intelligence Layer that exposes patient and operational signals as first-class events.
• Live workstreams in clinical documentation, referral-source conversion modeling, scheduling intelligence, and clinician productivity.
• A national virtual IOP serving patients across 41 states, with the regulated data and outcome signal needed to train, evaluate, and improve real systems.

Why this role is hard

Behavioral health is one of the most demanding applied-AI environments. Patients are often in acute distress. The regulatory surface spans HIPAA, state-by-state telehealth and licensure law, and rising FDA scrutiny of clinical decision support. AI systems in this environment must be observable, reversible, and defensible to clinicians, to regulators, and to internal review.

We are looking for an engineer who finds those constraints clarifying rather than limiting.

Requirements

• 10+ years of software engineering experience, with several recent years focused on LLM- or ML-powered systems in production.
• Hands-on experience designing and operating agentic systems, including tool use, planning, multi-agent orchestration, eval design, and failure-mode analysis.
• Track record of owning a complex technical initiative end-to-end, including evaluation infrastructure, observability, on-call, and regulatory or compliance review.
• Strong communication skills and comfort operating across registers, from executive strategy conversations to engineering code review.
• Genuine interest in healthcare and in applying AI responsibly in a regulated, high-stakes domain.
• BS, MS, or PhD in Computer Science, Machine Learning, Engineering, Mathematics, or a related technical field, or equivalent practical experience.
• Able to work a hybrid schedule of 4 days/week in our NYC office and located within 75 minutes' commuting distance of the office

Benefits

Charlie Health is pleased to offer comprehensive benefits to all full-time, exempt employees. Read more about our benefits here.

Additional Information

The total target base compensation for this role will be between $350,000 and $600,000 per year at the commencement of employment. Please note, pay will be determined on an individualized basis and will be impacted by location, experience, expertise, internal pay equity, and other relevant business considerations. Further, cash compensation is only part of the total compensation package, which, depending on the position, may include stock options and other Charlie Health-sponsored benefits.  #LI-HYBRID

About the role

The next generation of behavioral health will be built on agentic AI — systems that don't just predict or summarize, but act: scheduling, coordinating care, drafting clinical documentation, surfacing risk, and orchestrating the hundreds of micro-decisions that today consume clinician and operator time.

Charlie Health's ambition is to build the most advanced agentic systems in healthcare. We are investing in a multi-agent architecture grounded in a real-time clinical signal graph, governed by HIPAA-native infrastructure, and deployed against measurable patient outcomes in a national virtual IOP.

We are hiring a Senior Principal AI Engineer to lead the design, development, and deployment of these systems across our platform. This is a hands-on technical leadership role with deep cross-functional reach into Engineering, Product, Data, Clinical, and executive leadership.

Responsibilities

• Serve as the technical owner of agentic AI at Charlie Health, from architecture and model strategy through production deployment and ongoing reliability.
• Lead the development of clinical operations agents that handle scheduling, intake, insurance verification, referral triage, and care coordination, replacing brittle human handoffs with auditable, supervised AI workflows.
• Lead the development of clinician copilots for real-time documentation, risk surfacing, and treatment-plan support, integrated with our EHR and clinical data platform.
• Define and build the agentic substrate: eval frameworks, observability, guardrails, tool registries, prompt and policy versioning, PHI-safe logging, and simulation environments.
• Own the model strategy across frontier models, fine-tunes, distilled specialists, and classic ML, and articulate it clearly to executive and clinical leadership.
• Partner with Product, Clinical, and Data teams to identify high-leverage opportunities for AI across patient and operational experiences.
• Establish best practices around AI reliability, safety, evaluation, and explainability in a regulated clinical environment.
• Mentor engineers and data scientists across the organization and help foster a strong culture of technical excellence and responsible AI innovation.

What you'll work with

• An executive team that has committed to an AI-native operating model and is funding it.
• An AI Guild of senior engineers, data scientists, and clinical operators piloting AI across the company today.
• A modern data foundation including Snowflake, dbt, pgvector, and an emerging Intelligence Layer that exposes patient and operational signals as first-class events.
• Live workstreams in clinical documentation, referral-source conversion modeling, scheduling intelligence, and clinician productivity.
• A national virtual IOP serving patients across 41 states, with the regulated data and outcome signal needed to train, evaluate, and improve real systems.

Why this role is hard

Behavioral health is one of the most demanding applied-AI environments. Patients are often in acute distress. The regulatory surface spans HIPAA, state-by-state telehealth and licensure law, and rising FDA scrutiny of clinical decision support. AI systems in this environment must be observable, reversible, and defensible to clinicians, to regulators, and to internal review.

We are looking for an engineer who finds those constraints clarifying rather than limiting.

Requirements

• 10+ years of software engineering experience, with several recent years focused on LLM- or ML-powered systems in production.
• Hands-on experience designing and operating agentic systems, including tool use, planning, multi-agent orchestration, eval design, and failure-mode analysis.
• Track record of owning a complex technical initiative end-to-end, including evaluation infrastructure, observability, on-call, and regulatory or compliance review.
• Strong communication skills and comfort operating across registers, from executive strategy conversations to engineering code review.
• Genuine interest in healthcare and in applying AI responsibly in a regulated, high-stakes domain.
• BS, MS, or PhD in Computer Science, Machine Learning, Engineering, Mathematics, or a related technical field, or equivalent practical experience.
• Able to work a hybrid schedule of 4 days/week in our NYC office and located within 75 minutes' commuting distance of the office

Benefits

Charlie Health is pleased to offer comprehensive benefits to all full-time, exempt employees. Read more about our benefits here.

Additional Information

The total target base compensation for this role will be between $350,000 and $600,000 per year at the commencement of employment. Please note, pay will be determined on an individualized basis and will be impacted by location, experience, expertise, internal pay equity, and other relevant business considerations. Further, cash compensation is only part of the total compensation package, which, depending on the position, may include stock options and other Charlie Health-sponsored benefits.  #LI-HYBRID

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

Before Stripe, every growing internet platform had a payments team. Today, every growing internet platform has an Identity team. Identity verification has become core economic infrastructure for online businesses — enabling them to digitally verify their users' identities for fraud prevention, regulatory compliance, and trust and safety purposes.

At Stripe, we're building something more ambitious than a verification product. Our vision is to build the world's most trusted store of verified identities — a single, global, reusable, risk-scored representation of every individual who interacts with Stripe and beyond. This Identity Store becomes a platform with compounding network effects: more verifications enrich the store; a richer store produces better fraud signals; better signals mean lower friction for trusted users; lower friction drives more adoption.

Our product today serves millions of verifications per month for customers like OpenAI, Shopify, and WhatNot, and powers identity verification across multiple internal Stripe products including Fraud, Link, Connect, and Crypto. The problems ahead — adversarial AI and deepfake defense, networked identity infrastructure, progressive verification platforms, and low friction KYC APIs — sit at the frontier of what's possible in identity engineering.

What you’ll do

As a Staff Engineer on the Identity team, you will own the technical direction of one of Stripe's most strategically important product areas. This is not a role defined by task execution — it's a role defined by technical judgment, architectural influence, and the ability to turn an ambitious vision into a reliable, scalable system.

Responsibilities

• Own technical strategy — Define the architecture for Identity's core systems (Identity Store, headless verification platform, fraud detection infrastructure), make build-vs-buy-vs-partner decisions, and surface technical risks before they become customer-facing failures
• Lead 0-1 platform development — Architect net-new systems including the Identity Store, Networked Identity platform, and advanced fraud detection pipeline (behavioral biometrics, deepfake discriminators, adversarial training). Drive the evolution from a hosted-UI product to a headless-API and embedded-components platform
• Drive cross-functional alignment — Be the primary technical voice in roadmap and resourcing discussions. Partner with ML, product, legal, and engineering leads across Risk, Fraud, Link, Connect, and Crypto to ensure Identity's abstractions serve the full breadth of Stripe's product surface
• Raise the technical bar — Set engineering standards across ML, backend, mobile, and front-end; lead design reviews; and mentor senior engineers on the judgment needed to operate a high-stakes, externally-facing product
• Operate with ownership — Be accountable for reliability, scalability, and security of the systems you design — including the observability and alerting that catch model drift and fraud pattern shifts before customers do

Who you are

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• 10+ years of software engineering experience, including 5+ years of experience in a strategic technical leadership role.
• Experience leading engineering team(s) working on API design, abstractions, frameworks, or client libraries (e.g. building internal or external developer products). 
• Proven track record of delivering pragmatic solutions that accelerate business growth 
• Ability to adjust conversations from high-level discussions to detailed coding 
• Thrives on a high level of autonomy and responsibility 
• Clear and persuasive writing and in-person communication 
• Strong problem-solving skills, critical thinking, determination, and a growth mindset 
• Ability to work effectively with a diverse group of people, genuinely caring for each other and contributing to high level of psychological safety for all team members and partner
• Has experience in a range of software languages and frameworks. Examples of what we work with include:
• Languages: primarily Ruby and Typescript, but also Python, Scala, Java, Go, and SQL (Spark and Trino dialects)
• Frameworks: React, GraphQL Kafka, Protobuf, Airflow, Bazel, Temporal, 
• Others: Mongo, Splunk, Prometheus

Preferred qualifications

• Experience with identity verification, fraud detection, or trust and safety systems — especially systems with adversarial inputs at scale
• Familiarity with ML systems in production: model serving, training pipelines, observability, drift detection, and the feedback loops that keep models accurate under adversarial conditions
• Experience building platform products with multiple consumer types — internal platform teams and external developers — where API backward compatibility and abstraction quality are first-class concerns
• Prior experience with 0-1 product builds: you've started with a blank page and ended with a production system, and you have clear opinions about what makes that process go well or poorly
• Exposure to the technical stack we work with: primarily Ruby and TypeScript on the backend and web, Python for ML and data pipelines, with infrastructure spanning Kafka, Temporal, Protobuf, React, and distributed data stores
• Experience navigating ambiguity in a fast-moving organization — you can make confident technical decisions with incomplete information and update gracefully when new constraints emerge

Why Stripe Identity

• The market is at an inflection point. The global identity verification market is growing at ~15% CAGR, from $12.5B in 2024 to $21.8B by 2028. Competitors have more features today — but none of them have Stripe's moat: payments signals, fraud network, consumer graph, and KYC data spanning the full financial ecosystem. We're building to win the next decade, not the last one.
• The problems are genuinely hard. Adversarial ML against deepfakes and face swaps. Distributed identity graph architecture with real-time freshness requirements. Progressive verification APIs that minimize friction without sacrificing fraud detection accuracy. These are unsolved problems — not just at Stripe, but in the industry.
• You'll work on systems that matter immediately. The verifications we run protect real users from real fraud. When our recall improves by 10 points, that's millions of dollars in prevented fraud losses for customers who rely on us. When our mobile SDK completion rate rises, thousands more legitimate users successfully onboard each month.
• The team is small enough that your judgment shapes the architecture. This isn't a role where you inherit decisions made five years ago. It's a role where the decisions you make in 2026 are the ones the team will build on through 2029.

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

The Stripe Assistant team is transforming how users interact with Stripe by building an intelligent and proactive assistant that not only answers users’ queries but efficiently resolves issues and provides valuable business insights. We leverage LLMs and agentic systems to elevate the user experience across Stripe—from the dashboard to support surfaces—and we enable other teams to build and integrate their AI agents on our platform. We’re evolving from a helpful support tool to a trusted pilot that anticipates, optimizes, and executes on behalf of our users.

What you’ll do

As a Senior Machine Learning Engineer on the Stripe Assistant team, you’ll own the end-to-end ML and agent architecture that makes Stripe Assistant safe, reliable, and deeply useful. You’ll set the strategy for how the Assistant executes high-trust actions, delivers accurate analytical answers across Stripe and the broader web, orchestrates capabilities across many tools and agents, and grounds responses in authoritative Stripe and user data—so users can resolve issues quickly and confidently.

You’ll drive conversation continuity and personalization across surfaces, evolve the Assistant into a proactive partner that anticipates user needs, and deepen its presence in the dashboard to streamline critical workflows. You’ll establish rigorous evaluation and SLOs and deliver step‑change improvements in quality, latency, cost, and availability—paving the way for configurable levels of autonomy and, ultimately, a dependable operating layer over a merchant’s Stripe account.

Responsibilities

Our team operates fluidly and here are some problems you may tackle:

• Establish trustworthy, human-in-the-loop execution for high-trust “write” actions—prioritizing user control, transparency, accountability, and auditability so customers can delegate with confidence.
• Define and evolve the Assistant’s capability and governance model across hundreds of tools and agents, balancing power, permissions, and consistency at scale.
• Raise answer quality and usefulness by grounding in authoritative Stripe knowledge and live user data, building cross-surface memory and personalization, and making the Assistant proactive and present in the dashboard.
• Explore and apply optimal machine learning methods to improve Stripe Assistant’s overall performance, including but not limited to fine-tuning LLMs with RLHF, synthetic data generation, optimizing RAG pipelines via domain‑specific embedding and retriever fine‑tuning, and automatic prompt tuning, etc.
• Make quality and reliability a product: set and meet SLOs, build rigorous evaluation and benchmarking loops, and drive sustained improvements in latency, cost, and availability.
• Lead as a tech lead: mentor and grow engineers, uphold high bars for code quality, security, observability, and operational rigor, and align cross‑functionally to ship safely and fast.

Who you are

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• 5+ years in AI/ML and backend engineering.
• Applied LLM experience: RAG/embeddings, tool use/function calling, agentic planning/orchestration, fine-tuning, code generation, evaluations, etc.
• Proficient in Python (Ruby is a plus); strong distributed systems fundamentals.
• Experience working closely with product management, design, other engineers, and other cross-functional partners.

Preferred qualifications

• Experience operating ML systems at global scale with stringent SLOs—balancing reliability, latency, and cost—with privacy, security, and compliance by design.
• Experience building products where AI/ML is core; as well as balancing short-term product priorities with long-term AI/ML improvements.
• Track record building ML platforms, especially those that enable multiple teams to collaborate together.
• Strong technical leadership and communication: mentoring and elevating engineers, elevating AI/ML awareness and posture within organizations, setting architectural direction, and driving alignment in ambiguity.

Join us to build a trustworthy, proactive AI operating layer for every Stripe merchant—advancing safety, reliability, and insight at global scale. If you’re ready to help take Stripe Assistant from copilot to full autopilot and shape how businesses connect with Stripe, we’d love to hear from you.

The mission of our Solution Engineering team at Gong is to serve as trusted partners, advising on complex workflows and guiding customers toward an AI-first approach to their go-to-market strategy, ultimately helping them achieve their desired business outcomes.

As a Lead Solution Engineer on the team, you will be instrumental in creating raving fans among our prospects and customers. Your focus will be on helping them understand how our platform can drive their strategic business initiatives across the organization. 

RESPONSIBILITIES

• Work alongside Sales and Customer Success as the ultimate product expert and account team technical advisor.
• Thoroughly understand all aspects of the Gong platform in order to explain and demo the technology to all types of users.
• Be the trusted advisor to both prospects and customers to ensure they get the correct business outcome and value from our Gong technology, including business value quantification.
• Assist prospects and customers with business assessments (POCs, Pilots).
• Craft content to show customers how to leverage Gong for specific use cases.
• Execute the build and delivery of outcome-based demos and solution architecture diagrams for customers that have highly complex use cases.
• Solution complex business and technical requirements and explain them in simple terms.
• Assist with RFPs and questionnaires in the context of sales cycles.
• Help eliminate bottlenecks by working cross-functionally (Sales, Customer Success, Support, Product Management, Engineering) in order to get deals across the line.
• Partner with and support Marketing on demand gen activities (webinars, conferences, events).
• Contribute reusable assets to the Sales Engineering team and the company at-large.
• Collaborate closely with Product Marketing, Product Management, and Engineering to share voice of the customer feedback in order to influence our product roadmap and GTM strategies.

QUALIFICATIONS 

• 8+ years of customer facing experience in a solution engineering role (pre-sales) or a combination of solution engineering and other relevant work experience.
• Experience with sales technology: CRMs, cloud architecture, or telephony / web conference systems.
• Experience with data flows, AI & ML concepts, analytics, APIs, webhooks, or JSON is a plus.
• Understanding of security and privacy pertaining to SaaS systems.
• Understanding of key sales personas and workflows (including forecasting and prospecting) within a GTM organization.
• Excellent verbal and written communication skills.
• Strong interpersonal and teamwork skills.
• Able to make the complex sound simple!

PERKS & BENEFITS 

• We offer Gongsters a variety of medical, dental, and vision plans, designed to fit you and your family’s needs.
• Wellbeing Fund - flexible wellness stipend to support a healthy lifestyle.
• Mental Health benefits with covered therapy and coaching.
• 401(k) program to help you invest in your future.
• Education & learning stipend for personal growth and development.
• Flexible vacation time to promote a healthy work-life blend.
• Paid parental leave to support you and your family.
• Company-wide recharge days each quarter.
• Work from home stipend to help you succeed in a remote environment.

The target OTE for these positions is $205,000-$220,000.

Compensation is based on factors unique to each candidate, including, but not limited to, job-related skills, qualification, education, experience, and location. At Gong, we have a location-based compensation structure, which means there may be a different range for candidates in other locations. The total compensation package for this position, in addition to base compensation, may include incentive compensation, bonus, equity, and benefits. Some of our sales compensation programs also offer the potential to achieve above targeted earnings for those who exceed their sales targets. 

We are always looking for outstanding Gongsters! So if this sounds like something that interests you regardless of compensation, please reach out. We may have more roles for you to consider and would love to connect.

We have noticed a rise in recruiting impersonations across the industry, where scammers attempt to access candidates' personal and financial information through fake interviews and offers. All Gong recruiting email communications will always come from the @gong.io domain. Any outreach claiming to be from Gong via other sources should be ignored.

Gong is an equal-opportunity employer. We believe that diversity is integral to our success, and do not discriminate based on race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, military status, genetic information, or any other basis protected by applicable law.

To review Gong's privacy policy, visit https://www.gong.io/gong-io-job-candidates-privacy-notice/ for more details.

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

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

Brex’s Governance, Risk, and Compliance function is at an exciting and pivotal point in our maturity journey and we’re seeking a team member who can seamlessly bridge compliance expertise with technical execution. As a Senior GRC Engineer, you will drive critical GRC processes that mitigate risk, keep us compliant, and build trust with our customers and partners. You'll evolve the technical foundation of our Trust program by automating security controls, building integrations between security tools and GRC platforms, and creating scalable processes that enable Brex to maintain compliance efficiently as we expand into new markets. You'll work at the intersection of security, engineering, and compliance — translating regulatory requirements into technical solutions and building automation that eliminates manual toil.

You'll leverage your deep understanding of SOC 2, PCI DSS, ISO 27001, AI governance frameworks, and others to both design controls for emerging compliance requirements and mature existing programs through automation and continuous monitoring. You’ll support Trust Assurance, Third Party Risk Management, and other Security Risk Management initiatives. Working with our Engineering, Infrastructure, and Product teams, you'll translate compliance frameworks into technical controls and build automated systems that help us achieve world-class security as Brex expands.

Your contributions will directly accelerate Brex's maturity. You'll design workflows using Tines, build integrations between security and GRC systems, and create dashboards for security metrics. You'll implement controls across the technology stack, support multiple audits (SOC 2, PCI DSS, SOX/ITGC, FINRA, ISO), and contribute to AI governance framework implementation (ISO 42001, NIST AI RMF, EU AI Act).

You'll have autonomy to build innovative solutions, collaborating cross-functionally to implement controls that enable growth while communicating technical concepts effectively across the organization.

Where you’ll work

This role will be based in our Vancouver 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

• Manage and scale IT infrastructure, services and tooling
• Work with a diverse group of  IT partners to optimize our provided services
• Implement new services in support of Information Technologies vision
• Scale our services by implementing configuration as code via Terraform providers or APIs
• Operationalize and upskill IT and its partners by producing documentation and leading training sessions
• Evangelize best practices both internally and externally facing

Requirements

• 5+ years of experience in GRC, IT Governance, or Security Engineering with a strong track record of automating manual compliance workflows.
• Deep experience with security frameworks such as SOC 2, PCI DSS, ISO 27001, and NIST CSF, specifically within cloud-native environments.
• Technical proficiency in Python (or similar scripting languages) and experience building integrations using APIs to connect security tools with GRC systems. You can read code, design integrations, and understand technical implementations.
• Builder mindset with the ability to design and implement automated control testing, continuous monitoring, and data-driven security metrics. You see manual processes and immediately think about how to automate them.
• Exceptional cross-functional collaboration and communication skills. You can translate complex compliance requirements into technical specifications that engineering teams can actually implement and influence stakeholders across technical and non-technical domains.
• Strong systems thinking. You have the ability to design scalable GRC architectures that grow with the company, rather than just solving for the immediate audit.
• Bias for action. You’re a self-starter who ships solutions quickly and iterates based on feedback. 

Bonus points

• Previous experience in Fintech or banking environments navigating complex regulatory landscapes.
• Hands-on experience with Tines or other SOAR platforms to automate security operations.
• Familiarity with AI/ML governance frameworks (NIST AI RMF, ISO 42001) or securing agentic systems.
• Deep knowledge of Cloud Security (AWS/GCP), infrastructure-as-code (Terraform), or DevSecOps practices.
• Relevant industry certifications such as CISSP, CISA, or CCSP.
• Experience building metrics dashboards for security visualization and reporting.
• Active contributions to the GRC or Security community through open-source projects or public research.

Compensation

The expected salary range for this role is $153,600 - $192,000 CAD. 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.

Engineering

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

Brex’s Governance, Risk, and Compliance function is at an exciting and pivotal point in our maturity journey and we’re seeking a team member who can seamlessly bridge compliance expertise with technical execution. As a Senior GRC Engineer, you will drive critical GRC processes that mitigate risk, keep us compliant, and build trust with our customers and partners. You'll evolve the technical foundation of our Trust program by automating security controls, building integrations between security tools and GRC platforms, and creating scalable processes that enable Brex to maintain compliance efficiently as we expand into new markets. You'll work at the intersection of security, engineering, and compliance — translating regulatory requirements into technical solutions and building automation that eliminates manual toil.

You'll leverage your deep understanding of SOC 2, PCI DSS, ISO 27001, AI governance frameworks, and others to both design controls for emerging compliance requirements and mature existing programs through automation and continuous monitoring. You’ll support Trust Assurance, Third Party Risk Management, and other Security Risk Management initiatives. Working with our Engineering, Infrastructure, and Product teams, you'll translate compliance frameworks into technical controls and build automated systems that help us achieve world-class security as Brex expands.

Your contributions will directly accelerate Brex's maturity. You'll design workflows using Tines, build integrations between security and GRC systems, and create dashboards for security metrics. You'll implement controls across the technology stack, support multiple audits (SOC 2, PCI DSS, SOX/ITGC, FINRA, ISO), and contribute to AI governance framework implementation (ISO 42001, NIST AI RMF, EU AI Act).

You'll have autonomy to build innovative solutions, collaborating cross-functionally to implement controls that enable growth while communicating technical concepts effectively across the 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

• Manage and scale IT infrastructure, services and tooling
• Work with a diverse group of  IT partners to optimize our provided services
• Implement new services in support of Information Technologies vision
• Scale our services by implementing configuration as code via Terraform providers or APIs
• Operationalize and upskill IT and its partners by producing documentation and leading training sessions
• Evangelize best practices both internally and externally facing

Requirements

• 5+ years of experience in GRC, IT Governance, or Security Engineering with a strong track record of automating manual compliance workflows.
• Deep experience with security frameworks such as SOC 2, PCI DSS, ISO 27001, and NIST CSF, specifically within cloud-native environments.
• Technical proficiency in Python (or similar scripting languages) and experience building integrations using APIs to connect security tools with GRC systems. You can read code, design integrations, and understand technical implementations.
• Builder mindset with the ability to design and implement automated control testing, continuous monitoring, and data-driven security metrics. You see manual processes and immediately think about how to automate them.
• Exceptional cross-functional collaboration and communication skills. You can translate complex compliance requirements into technical specifications that engineering teams can actually implement and influence stakeholders across technical and non-technical domains.
• Strong systems thinking. You have the ability to design scalable GRC architectures that grow with the company, rather than just solving for the immediate audit.
• Bias for action. You’re a self-starter who ships solutions quickly and iterates based on feedback. 

Bonus points

• Previous experience in Fintech or banking environments navigating complex regulatory landscapes.
• Hands-on experience with Tines or other SOAR platforms to automate security operations.
• Familiarity with AI/ML governance frameworks (NIST AI RMF, ISO 42001) or securing agentic systems.
• Deep knowledge of Cloud Security (AWS/GCP), infrastructure-as-code (Terraform), or DevSecOps practices.
• Relevant industry certifications such as CISSP, CISA, or CCSP.
• Experience building metrics dashboards for security visualization and reporting.
• Active contributions to the GRC or Security community through open-source projects or public research.

Compensation

The expected salary range for this role is $153,600 - $192,000. 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.

Engineering

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

Brex’s Governance, Risk, and Compliance function is at an exciting and pivotal point in our maturity journey and we’re seeking a team member who can seamlessly bridge compliance expertise with technical execution. As a Senior GRC Engineer, you will drive critical GRC processes that mitigate risk, keep us compliant, and build trust with our customers and partners. You'll evolve the technical foundation of our Trust program by automating security controls, building integrations between security tools and GRC platforms, and creating scalable processes that enable Brex to maintain compliance efficiently as we expand into new markets. You'll work at the intersection of security, engineering, and compliance — translating regulatory requirements into technical solutions and building automation that eliminates manual toil.

You'll leverage your deep understanding of SOC 2, PCI DSS, ISO 27001, AI governance frameworks, and others to both design controls for emerging compliance requirements and mature existing programs through automation and continuous monitoring. You’ll support Trust Assurance, Third Party Risk Management, and other Security Risk Management initiatives. Working with our Engineering, Infrastructure, and Product teams, you'll translate compliance frameworks into technical controls and build automated systems that help us achieve world-class security as Brex expands.

Your contributions will directly accelerate Brex's maturity. You'll design workflows using Tines, build integrations between security and GRC systems, and create dashboards for security metrics. You'll implement controls across the technology stack, support multiple audits (SOC 2, PCI DSS, SOX/ITGC, FINRA, ISO), and contribute to AI governance framework implementation (ISO 42001, NIST AI RMF, EU AI Act).

You'll have autonomy to build innovative solutions, collaborating cross-functionally to implement controls that enable growth while communicating technical concepts effectively across the organization.

Where you’ll work

This role will be based in our New York 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

• Manage and scale IT infrastructure, services and tooling
• Work with a diverse group of  IT partners to optimize our provided services
• Implement new services in support of Information Technologies vision
• Scale our services by implementing configuration as code via Terraform providers or APIs
• Operationalize and upskill IT and its partners by producing documentation and leading training sessions
• Evangelize best practices both internally and externally facing

Requirements

• 5+ years of experience in GRC, IT Governance, or Security Engineering with a strong track record of automating manual compliance workflows.
• Deep experience with security frameworks such as SOC 2, PCI DSS, ISO 27001, and NIST CSF, specifically within cloud-native environments.
• Technical proficiency in Python (or similar scripting languages) and experience building integrations using APIs to connect security tools with GRC systems. You can read code, design integrations, and understand technical implementations.
• Builder mindset with the ability to design and implement automated control testing, continuous monitoring, and data-driven security metrics. You see manual processes and immediately think about how to automate them.
• Exceptional cross-functional collaboration and communication skills. You can translate complex compliance requirements into technical specifications that engineering teams can actually implement and influence stakeholders across technical and non-technical domains.
• Strong systems thinking. You have the ability to design scalable GRC architectures that grow with the company, rather than just solving for the immediate audit.
• Bias for action. You’re a self-starter who ships solutions quickly and iterates based on feedback. 

Bonus points

• Previous experience in Fintech or banking environments navigating complex regulatory landscapes.
• Hands-on experience with Tines or other SOAR platforms to automate security operations.
• Familiarity with AI/ML governance frameworks (NIST AI RMF, ISO 42001) or securing agentic systems.
• Deep knowledge of Cloud Security (AWS/GCP), infrastructure-as-code (Terraform), or DevSecOps practices.
• Relevant industry certifications such as CISSP, CISA, or CCSP.
• Experience building metrics dashboards for security visualization and reporting.
• Active contributions to the GRC or Security community through open-source projects or public research.

Compensation

The expected salary range for this role is $153,600 - $192,000. 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.

Engineering

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

Brex’s Governance, Risk, and Compliance function is at an exciting and pivotal point in our maturity journey and we’re seeking a team member who can seamlessly bridge compliance expertise with technical execution. As a Senior GRC Engineer, you will drive critical GRC processes that mitigate risk, keep us compliant, and build trust with our customers and partners. You'll evolve the technical foundation of our Trust program by automating security controls, building integrations between security tools and GRC platforms, and creating scalable processes that enable Brex to maintain compliance efficiently as we expand into new markets. You'll work at the intersection of security, engineering, and compliance — translating regulatory requirements into technical solutions and building automation that eliminates manual toil.

You'll leverage your deep understanding of SOC 2, PCI DSS, ISO 27001, AI governance frameworks, and others to both design controls for emerging compliance requirements and mature existing programs through automation and continuous monitoring. You’ll support Trust Assurance, Third Party Risk Management, and other Security Risk Management initiatives. Working with our Engineering, Infrastructure, and Product teams, you'll translate compliance frameworks into technical controls and build automated systems that help us achieve world-class security as Brex expands.

Your contributions will directly accelerate Brex's maturity. You'll design workflows using Tines, build integrations between security and GRC systems, and create dashboards for security metrics. You'll implement controls across the technology stack, support multiple audits (SOC 2, PCI DSS, SOX/ITGC, FINRA, ISO), and contribute to AI governance framework implementation (ISO 42001, NIST AI RMF, EU AI Act).

You'll have autonomy to build innovative solutions, collaborating cross-functionally to implement controls that enable growth while communicating technical concepts effectively across the organization.

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

• Manage and scale IT infrastructure, services and tooling
• Work with a diverse group of  IT partners to optimize our provided services
• Implement new services in support of Information Technologies vision
• Scale our services by implementing configuration as code via Terraform providers or APIs
• Operationalize and upskill IT and its partners by producing documentation and leading training sessions
• Evangelize best practices both internally and externally facing

Requirements

• 5+ years of experience in GRC, IT Governance, or Security Engineering with a strong track record of automating manual compliance workflows.
• Deep experience with security frameworks such as SOC 2, PCI DSS, ISO 27001, and NIST CSF, specifically within cloud-native environments.
• Technical proficiency in Python (or similar scripting languages) and experience building integrations using APIs to connect security tools with GRC systems. You can read code, design integrations, and understand technical implementations.
• Builder mindset with the ability to design and implement automated control testing, continuous monitoring, and data-driven security metrics. You see manual processes and immediately think about how to automate them.
• Exceptional cross-functional collaboration and communication skills. You can translate complex compliance requirements into technical specifications that engineering teams can actually implement and influence stakeholders across technical and non-technical domains.
• Strong systems thinking. You have the ability to design scalable GRC architectures that grow with the company, rather than just solving for the immediate audit.
• Bias for action. You’re a self-starter who ships solutions quickly and iterates based on feedback. 

Bonus points

• Previous experience in Fintech or banking environments navigating complex regulatory landscapes.
• Hands-on experience with Tines or other SOAR platforms to automate security operations.
• Familiarity with AI/ML governance frameworks (NIST AI RMF, ISO 42001) or securing agentic systems.
• Deep knowledge of Cloud Security (AWS/GCP), infrastructure-as-code (Terraform), or DevSecOps practices.
• Relevant industry certifications such as CISSP, CISA, or CCSP.
• Experience building metrics dashboards for security visualization and reporting.
• Active contributions to the GRC or Security community through open-source projects or public research.

Compensation

The expected salary range for this role is $153,600 - $192,000. 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 Team + The Role

Pendo’s Customer Engineering team is the technical backbone of the pre- and post-sales customer motion. The team brings together work that has historically sat across Customer Success, Technical Account Management, and Solutions Engineering into one full-lifecycle technical owner. Customer Engineering helps customers connect technical execution to business outcomes and realize the value of their investment in Pendo.

As a Senior Customer Engineer, you own strategic and complex Enterprise accounts across acquisition, implementation, adoption, expansion, and escalation. You lead technical strategy for complex deployments, resolve ambiguous customer challenges, and partner closely with account teams to drive retention, expansion, and customer value. Your impact extends beyond your own accounts through playbooks, coaching, repeatable fixes, and workflow improvements that raise the capability of the broader Customer Engineering team.

This is a remote role supporting a East Coast territory. 

What this looks like day-to-day

• New customer acquisition and selling: Partner with Account Directors on pre-sales motions for new customer acquisition, product expansion, and expansion across lines of business. You identify customer pain, craft and deliver tailored demos, and lead technical evaluations through requirements gathering, success criteria, installation guidance, and hands-on workshops.
• Complex implementations and value realization: Lead onboarding for strategic customers, including planning, implementation, integrations, and training. You build customer-specific implementation playbooks, guide complex deployments with executive visibility, and ensure customers realize value during onboarding and over time.
• Adoption, expansion, and growth: Identify opportunities to expand adoption across underutilized features, untapped products, and new lines of business. When adoption stalls, you diagnose root causes, design a path forward, and connect technical work to measurable business outcomes.
• Technical escalation: Serve as an escalation point for complex technical challenges raised by other Customer Engineers. You resolve ambiguous problems with multiple variables and tradeoffs, involving Product or Engineering only when truly needed.
• Team capability building: Build playbooks, document repeatable fixes, coach peers, and improve how Customer Engineering work gets done across the team. You use AI tools to accelerate content creation, surface patterns across accounts, and share practical workflow findings with the broader team.
• Early account impact: Assess account health, risks, stakeholder relationships, and the highest-priority problems to solve within your portfolio. You establish trust with Account Directors and customer stakeholders, then independently lead complex customer interactions that move accounts forward.
• Quarterly business outcomes: Drive measurable improvement in customer health, retention, expansion, and technical resolution across strategic accounts. You contribute reusable frameworks, playbooks, or processes that other Customer Engineers use and become a trusted advisor to Account Directors on account health and technical strategy.

Who You Are

Beyond the qualifications, we hire through a specific lens. These aren't buzzwords; they're the things we'll actually look for in how you talk about your work.

You're a builder, not a maintainer.

You're most energized when there isn't a clear path yet, and you get to define it. You don't wait for direction; you identify gaps, shape solutions, and drive them forward. At Pendo, great Senior Customer Engineers don't just follow instructions; they operate as strategic advisors, influencing decisions, guiding stakeholders, and elevating how we work.

You're AI-curious - genuinely.

You're not using AI tools occasionally. You're rewiring how you work around them. You're faster, sharper, and more prolific because of it, and you bring that energy to everything — how you approach your work, how you prep, how you communicate, how you think. We want someone who sees AI as a multiplier, not a shortcut.

Must-haves

• 5 to 8 years of experience in a customer-facing technical role such as Sales Engineering, Solutions Engineering, Technical Account Management, Customer Engineering, implementation, professional services, or solutions architecture.
• Deep hands-on experience with web technologies including HTML, CSS, JavaScript, and REST APIs, with the ability to architect and troubleshoot complex front-end implementations.
• Proven experience managing strategic Enterprise accounts with executive-level stakeholders, competing priorities, and complex technical or business requirements.
• Demonstrated ability to solve ambiguous problems involving multiple variables, constraints, and tradeoffs.
• Experience connecting technical execution to business outcomes such as retention, expansion, ROI, and value realization.
• Hands-on use of AI tools for account research, content creation, or workflow automation, with demonstrated integration into day-to-day work rather than light experimentation.
• Experience mentoring or coaching more junior technical team members.

Nice-to-haves

• Experience with Pendo, digital adoption platforms, product analytics, or in-app experience tooling in an enterprise environment.
• Hands-on experience with mobile app development, including Swift, Android, or React Native.
• Familiarity with data architecture, AI or machine learning concepts, or advanced analytics instrumentation.
• Track record of building reusable frameworks, playbooks, or processes that were adopted by a broader team.

About Pendo

Pendo was founded in 2013 by former product managers, who combined their heads and hearts to build something they wanted but never had as product managers: a simple way to understand and attack what truly drives product success. Our mission is to improve society's experience with software. Come join one of the fastest-growing startups, supported by best-in-class institutions like Battery Ventures, Salesforce Ventures, Spark Capital and Meritech.

Pendo Core Values: Bias to Act, Hone Your Craft, The Team is Pendo, and Maniacal Focus.

Location: Remote

Compensation: The expected On-Target Earnings (OTE) range for this role is $193,393 - $241,742, with a 70/30 base-to-variable compensation split.

Benefits: Pendo offers highly competitive United States benefits, including employer-heavy coverage with $0 premium options, strong 401(k) match, equity, and flexible time off.

EEOC: We are an equal opportunity employer and believe having diverse teams where everyone brings their whole self to Pendo is key to our success. We welcome all people of different backgrounds, experiences, abilities and perspectives.

Accessibility: Pendo is committed to working with, and providing access and reasonable accommodation to, applicants with mental and/or physical disabilities. If you think you may require an accommodation for any part of the recruitment process, please send a request to: accommodation@pendo.io. All requests for accommodations are treated discreetly and confidentially, as practical and permitted by law.

About Us:

YipitData is the leading market research and analytics firm for the disruptive economy and recently raised up to $475M from The Carlyle Group at a valuation over $1B.

We analyze billions of alternative data points every day to provide accurate, detailed insights on ridesharing, e-commerce marketplaces, payments and more. Our on-demand insights team uses proprietary technology to identify, license, clean and analyze the data many of the world’s largest investment funds and corporations depend on.

For three years and counting, we have been recognized as one of Inc’s Best Workplaces. We are a fast-growing technology company backed by The Carlyle Group and Norwest Venture Partners. Our offices are located in NYC, Austin, Miami, Los Angeles (CA), Cupertino (CA), Hong Kong, Shanghai, Beijing, Guangzhou, and Singapore. We cultivate a people-centric culture focused on mastery, ownership, and transparency. About the Role:

We are seeking an AI Security Engineer to lead the implementation, monitoring, and continuous improvement of security, governance, and trust controls for AI systems across the organization. This role will focus on operationalizing AI system security controls using the Agentic Trust Framework mapped to OWASP guidance and the NIST AI RMF, with particular emphasis on observability engineering, behavioral monitoring, policy enforcement, misuse detection, and risk-informed response.

This person will serve as a bridge between Security, Engineering, Data, Platform, Compliance, and AI product teams to ensure AI systems are not only functional and performant, but also trustworthy, auditable, resilient, and aligned with enterprise governance requirements.

The ideal candidate combines technical depth in AI/ML systems, strong security and monitoring instincts, and the ability to define practical controls for complex, fast-evolving agentic and generative AI environments.

We expect U.S. based working hours with the majority of the team working East and Central Time Zones.

In this role, you will:

• Own AI behavior monitoring: Define what trustworthy and untrustworthy AI behavior looks like, and ensure it is measurable in production.
• Own AI observability standards: Establish telemetry, tracing, logging, and alerting requirements for AI systems and agentic workflows.
• Own control validation for agentic systems: Verify that guardrails, policy checks, access boundaries, and execution constraints are functioning as intended.
• Own AI security event analysis: Detect, investigate, and document suspicious, unsafe, or non-compliant AI behaviors and coordinate response.
• Own implementation support for governance frameworks: Translate governance principles into technical and operational requirements that product and platform teams can adopt.
• Own AI trust metrics and reporting: Define KPIs, KRIs, and dashboards that show leadership whether AI systems are operating within approved trust and security boundaries.
• Own continuous improvement of AI controls: Use incidents, testing, behavioral findings, and stakeholder feedback to strengthen control design and reduce residual risk over time.

You Are Likely To Succeed If:

• 5+ years of experience in one or more of the following: security engineering, detection engineering, observability engineering, site reliability engineering, application security, ML platform engineering, or AI governance implementation.
• Experience designing monitoring, logging, telemetry, or detection strategies for distributed systems, cloud services, or data-intensive applications.
• Familiarity with AI/ML system architecture, including large language models, retrieval-augmented generation, inference pipelines, model APIs, and agentic workflows.
• Experience translating governance, risk, or policy requirements into operational controls and measurable technical requirements.
• Strong understanding of security concepts such as identity and access management, least privilege, data protection, abuse prevention, auditability, and incident response.
• Experience investigating system behavior, identifying anomalies, and working cross-functionally to drive remediation.
• Hold industry certifications (or equivalent experience): CISSP, CCSP, GIAC Machine Learning Engineer (GMLE) 
• Strong written communication skills, including ability to write standards, control definitions, runbooks, and leadership-facing summaries.

Preferred Qualifications:

• Experience with AI observability tooling, tracing frameworks, or telemetry pipelines for LLM or agent-based systems.
• Experience implementing controls for AI safety, AI red teaming, prompt security, model misuse detection, or secure tool execution.
• Familiarity with Microsoft security, compliance, and AI governance ecosystems.
• Familiarity with trust and safety concepts for generative AI and autonomous systems.
• Experience supporting internal governance, risk, privacy, or compliance review processes for AI-enabled products.
• Experience building dashboards, alerts, and behavioral analytics for security or operational monitoring.
• Experience working in highly regulated or audit-sensitive environments.

What We Offer:

• Our compensation package includes comprehensive benefits, perks, and a competitive salary: 

  • We care about your personal life, and we mean it. We offer flexible work hours, flexible vacation, a generous 401K match, parental leave, team events, wellness budget, learning reimbursement, and more!
  • Your growth at YipitData is determined by the impact that you are making, not by tenure, unnecessary facetime, or office politics. Everyone at YipitData is empowered to learn, self-improve, and master their skills in an environment focused on ownership, respect, and trust. See more on our high-impact, high-opportunity work environment above!
  • The annual on-target earnings for this position is anticipated to be up to $230 ~ $280K. The final offer may be determined by a number of factors, including, but not limited to, the applicant's experience, knowledge, skills, abilities, as well as internal team benchmarks. 
  The compensation package also includes equity.

This role may be performed fully remotely within the United States. Please note that our US headquarters are located in NYC. We also have have US offices in Austin, Miami, Los Angeles (CA), and Cupertino (CA). If the remote work is performed outside of these offices, income may be subject to New York State tax withholding.

Please note that for this position, we are not able to consider candidates who currently or in the future will require visa sponsorship.

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal-opportunity employer.

Job Applicant Privacy Notice 

About Us:

YipitData is the leading market research and analytics firm for the disruptive economy and recently raised up to $475M from The Carlyle Group at a valuation over $1B.

We analyze billions of alternative data points every day to provide accurate, detailed insights on ridesharing, e-commerce marketplaces, payments and more. Our on-demand insights team uses proprietary technology to identify, license, clean and analyze the data many of the world’s largest investment funds and corporations depend on.

For three years and counting, we have been recognized as one of Inc’s Best Workplaces. We are a fast-growing technology company backed by The Carlyle Group and Norwest Venture Partners. Our offices are located in NYC, Austin, Miami, Los Angeles (CA), Cupertino (CA), Hong Kong, Shanghai, Beijing, Guangzhou, and Singapore. We cultivate a people-centric culture focused on mastery, ownership, and transparency. About the Role:

We are seeking an AI Security Engineer to lead the implementation, monitoring, and continuous improvement of security, governance, and trust controls for AI systems across the organization. This role will focus on operationalizing AI system security controls using the Agentic Trust Framework mapped to OWASP guidance and the NIST AI RMF, with particular emphasis on observability engineering, behavioral monitoring, policy enforcement, misuse detection, and risk-informed response.

This person will serve as a bridge between Security, Engineering, Data, Platform, Compliance, and AI product teams to ensure AI systems are not only functional and performant, but also trustworthy, auditable, resilient, and aligned with enterprise governance requirements.

The ideal candidate combines technical depth in AI/ML systems, strong security and monitoring instincts, and the ability to define practical controls for complex, fast-evolving agentic and generative AI environments.

We expect U.S. based working hours with the majority of the team working East and Central Time Zones.

In this role, you will:

• Own AI behavior monitoring: Define what trustworthy and untrustworthy AI behavior looks like, and ensure it is measurable in production.
• Own AI observability standards: Establish telemetry, tracing, logging, and alerting requirements for AI systems and agentic workflows.
• Own control validation for agentic systems: Verify that guardrails, policy checks, access boundaries, and execution constraints are functioning as intended.
• Own AI security event analysis: Detect, investigate, and document suspicious, unsafe, or non-compliant AI behaviors and coordinate response.
• Own implementation support for governance frameworks: Translate governance principles into technical and operational requirements that product and platform teams can adopt.
• Own AI trust metrics and reporting: Define KPIs, KRIs, and dashboards that show leadership whether AI systems are operating within approved trust and security boundaries.
• Own continuous improvement of AI controls: Use incidents, testing, behavioral findings, and stakeholder feedback to strengthen control design and reduce residual risk over time.

You Are Likely To Succeed If:

• 5+ years of experience in one or more of the following: security engineering, detection engineering, observability engineering, site reliability engineering, application security, ML platform engineering, or AI governance implementation.
• Experience designing monitoring, logging, telemetry, or detection strategies for distributed systems, cloud services, or data-intensive applications.
• Familiarity with AI/ML system architecture, including large language models, retrieval-augmented generation, inference pipelines, model APIs, and agentic workflows.
• Experience translating governance, risk, or policy requirements into operational controls and measurable technical requirements.
• Strong understanding of security concepts such as identity and access management, least privilege, data protection, abuse prevention, auditability, and incident response.
• Experience investigating system behavior, identifying anomalies, and working cross-functionally to drive remediation.
• Hold industry certifications (or equivalent experience): CISSP, CCSP, GIAC Machine Learning Engineer (GMLE) 
• Strong written communication skills, including ability to write standards, control definitions, runbooks, and leadership-facing summaries.

Preferred Qualifications:

• Experience with AI observability tooling, tracing frameworks, or telemetry pipelines for LLM or agent-based systems.
• Experience implementing controls for AI safety, AI red teaming, prompt security, model misuse detection, or secure tool execution.
• Familiarity with Microsoft security, compliance, and AI governance ecosystems.
• Familiarity with trust and safety concepts for generative AI and autonomous systems.
• Experience supporting internal governance, risk, privacy, or compliance review processes for AI-enabled products.
• Experience building dashboards, alerts, and behavioral analytics for security or operational monitoring.
• Experience working in highly regulated or audit-sensitive environments.

What We Offer:

• Our compensation package includes comprehensive benefits, perks, and a competitive salary: 

  • We care about your personal life, and we mean it. We offer flexible work hours, flexible vacation, a generous 401K match, parental leave, team events, wellness budget, learning reimbursement, and more!
  • Your growth at YipitData is determined by the impact that you are making, not by tenure, unnecessary facetime, or office politics. Everyone at YipitData is empowered to learn, self-improve, and master their skills in an environment focused on ownership, respect, and trust. See more on our high-impact, high-opportunity work environment above!
  • The annual on-target earnings for this position is anticipated to be up to $230 ~ $280K. The final offer may be determined by a number of factors, including, but not limited to, the applicant's experience, knowledge, skills, abilities, as well as internal team benchmarks. 
  The compensation package also includes equity.

This role may be performed fully remotely within the United States. Please note that our US headquarters are located in NYC. We also have have US offices in Austin, Miami, Los Angeles (CA), and Cupertino (CA). If the remote work is performed outside of these offices, income may be subject to New York State tax withholding.

Please note that for this position, we are not able to consider candidates who currently or in the future will require visa sponsorship.

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal-opportunity employer.

Job Applicant Privacy Notice 

Position Summary: 

Join our mission to infuse cutting-edge AI/ML/GenAI into pharmacy benefits as a Machine Learning Engineer.  We are looking for a software engineer with machine learning expertise to join us in expanding our AI capabilities, enabling increased productivity and magical experiences in our products and services. 

In this role you will be expected to design and implement complex AI systems that leverage ML models for NLP, NLG, multimodal data analysis, chatbots, and RAG-based QnA. The ideal candidate should be passionate about applying AI/ML concepts to difficult problems and develop scalable solutions. We want people who like working in a collaborative team environment and enjoy creating practical, efficient, and high-performance software that leverages Large Language Models (LLM), Multimodal Language Models(MLM), and other ML models and techniques to build amazing capabilities for our customers, partners, and employees. Most importantly, we are a mission-oriented, high-growth startup and we are looking for folks that are excited to be part of our journey to make lasting impact towards transforming healthcare. 

Responsibilities: 

• Develop and productionize machine learning (ML) solutions in the fields of Document understanding, Search and QnA, GenAI, Virtual Agents, etc. 

• Develop and maintain backend services using Python, focusing on AI-driven applications. 

• Design and implement APIs for seamless integration with AI models and services. 

• Develop tools for large-scale data processing and ETL and contribute to extracting insights from data to help guide ML systems development 

• Participate in code reviews, testing, and quality assurance processes. 

• Troubleshoot and resolve technical issues related to AI model, integration, deployment and backend services. 

• Develop algorithms to ensure the integrity and robustness of ML solutions by developing automated testing and validation processes. 

• Document and communicate development processes and implementation details with peers and supervisors 

• Ensure the security and compliance of healthcare data, adhering to HIPAA regulations. 

Required Qualifications: 

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related quantitative field. 

• 2+ years experience in the industry with a strong focus on ML solutions development and production deployment is a plus. 

• Good understanding of Machine Learning fundamentals such as Deep Neural Networks, Transformer-based LLM and MLMs, Boosted or standard decision trees, RAG, etc.  

• Strong grasp of OOP, Design Patterns, efficient algorithms, and quality software development. 

• Proficiency in Python and familiarity with ML libraries such as PyTorch. 

• Familiarity with GenAI services such as OpenAI GPT, Anthropic Claude, Google Gemma etc. Some experience in developing and fine-tuning prompts on any of the GenAI services is a plus. 

• Excellent problem-solving skills, attention to detail, and a strong capacity for logical thinking. 

• The ability to work collaboratively across multiple disciplines in an extremely fast-paced, startup environment. 

• Good written communication skills that enable collaboration in a remote environment. 

What you’ll do

As a Staff Software Engineer on the Identity & Access Management (IAM) team, you will be a primary architect of CoreWeave’s IAM strategy as we rapidly expand the platform. You will leverage your deep expertise in identity systems to design, extend, and harden a platform that secures our high-performance cloud infrastructure. This is a high-impact leadership role where you will balance rapid execution with the long-term technical vision necessary to meet the demands of our global enterprise customers.

About the role

• Technical Strategy & Vision: Partner with Product and Engineering Leadership to translate complex requirements into a multi-year technical roadmap that balances immediate business needs with long-term architectural integrity
• System Architecture: Lead the design and implementation of high-throughput, low-latency distributed systems for authentication and authorization across all CoreWeave services
• Platform Evolution: Architect customer-facing experiences for identity lifecycle management, IdP integration (SAML/OIDC), and granular permissioning models
• Engineering Excellence: Champion a security-first mindset across the organization, establishing rigorous standards for code quality, design reviews, and threat modeling
• Operational Leadership: Elevate the team’s operational maturity by defining SLOs, enhancing deep-stack observability, and driving automation to eliminate systemic toil
• Technical Mentorship: Act as a force multiplier by mentoring Senior engineers and fostering a culture of continuous learning and high-performance engineering
• Cross-Functional Influence: Drive alignment on critical security initiatives across the broader engineering organization, articulating complex technical trade-offs to stakeholders

Who you are

• 10+ Years of Experience building and operating large-scale distributed systems in a production environment using one or more of Go, TypeScript, or React
• 5+ Years of IAM Domain Expertise: Proven track record of developing identity products and a deep mastery of standards including OIDC, OAuth2.0, SAML, and JWT
• Technical Leadership: Demonstrated experience leading large, cross-functional projects from inception to planet-scale deployment with measurable business impact
• Cloud-Native Mastery: Deep understanding of Kubernetes, microservices architecture at scale, and advanced CI/CD patterns
• Enterprise Security Acumen: Solid knowledge of the security, compliance (SOC2, HIPAA, etc.), and governance needs of sophisticated enterprise customers
• Strategic Communication: Superb ability to synthesize ambiguous requirements into clear technical designs and communicate trade-offs to both executive and technical audiences
• Analytical Problem Solving: Expert-level ability to debug complex, intermittent issues within distributed environments

Preferred

• Architectural Transformation: Experience leading the decomposition of monolithic architectures into high-performance microservices
• AI-Augmented Development: Familiarity with leveraging AI agents (e.g., Claude, Cursor) to accelerate the software development lifecycle
• Emerging Tech Knowledge: Familiarity with AI/ML workloads, GPU-based infrastructure, and the unique security challenges of MLOps at scale
• Policy-as-Code: Experience with modern authorization frameworks such as Open Policy Agent (OPA) or Cedar

Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk. 

• You love to solve complex problems and build high-scale, low-latency distributed systems.
• You’re curious about how to evolve cloud platforms and scale infrastructure globally for the next generation of AI workloads.
• You’re an expert in one or more preferred programming languages (Go, TypeScript, Python), and have deep technical knowledge of distributed systems.

Why CoreWeave?

At CoreWeave, we work hard, have fun, and move fast!  We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: 

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best-in-Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us!

The base salary range for this role is $188,000 to $275,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). 

The Customer Problem

When you open Spotify, the homepage is different for every user. When you browse Nike's site, products are ranked by what Nike thinks you'll buy. When you log into Chase, offers are tailored to your spending. This is web and mobile personalization: deciding in real time what each customer sees inside your product. It directly moves conversion, retention, and revenue at the point where customers are already engaged.

Almost nobody does it well outside of companies that build it from scratch. A single experiment (testing whether high-spend users convert better seeing a loyalty upsell on the account page) requires an analyst to build the segment, a PM to write targeting rules, a designer to create variants, an engineer to instrument the test, and someone to monitor results. Most teams run 5–10 per quarter and end up with a handful of static rules covering a fraction of their users.

The tools that exist (Optimizely, Adobe Target) handle infrastructure but don't solve the coordination problem. Every experiment, audience, and piece of content still requires manual configuration. They also force companies to duplicate customer data into a proprietary store, adding months of implementation and permanent vendor lock-in.

Why Agents are the Solution

The shift from "AI assists a step" to "AI owns a workflow" is already happening in software engineering. Personalization is ready for the same shift.

Today: A growth team testing whether high-intent visitors convert better with a comparison page spends 2–4 weeks coordinating across tools and people. They run maybe 8 experiments that quarter.

With agents: The growth lead describes the goal. An agent identifies the audience from existing customer data, generates variants informed by past performance, configures the experiment with holdout groups, launches it, monitors significance, and reallocates traffic. The human reviews before launch. Calendar time: hours. The same team runs 50+ experiments per quarter because the coordination cost drops to nearly zero.

Agents handle the full chain as one workflow: audience, content, experiment, optimization. Instead of a bunch of manual steps, agents rethink this as a continuously improving closed loop.

Why Hightouch, Why Now

More than 1,000 enterprises—like Domino’s, Chime, Spotify, Ramp, Whoop, Grammarly, use Hightouch to run their marketing. Earlier this year we launched the Agentic Marketing Platform with two pillars:

• Agentic Advertising Studio. Agents analyze performance, competitor creative, and brand guidelines, then assemble ad concepts across Meta, Google, TikTok, and LinkedIn.
• Agentic Lifecycle Studio. Agents identify lifecycle opportunities, draft audiences, assemble on-brand email and SMS campaigns, and orchestrate them through Braze, Iterable, Salesforce, and Adobe.

Advertising covers paid media. Lifecycle covers email, SMS, and push. Agentic Personalization is the third pillar: what customers experience inside the website and mobile app. Different buyer (product and growth teams), different workflow (real-time experimentation), different technical surface (browser SDKs and edge delivery). Same agent infrastructure and data foundation.

What no competitor can replicate:

• Agent orchestration. 25+ specialized skills for audience building, content generation, campaign analysis, and multi-step workflow coordination.
• Content assembly. Agents search existing asset libraries for reusable on-brand content before generating anything new, which is what makes output trustworthy enough for enterprises to ship.
• Marketing and brand context. Persistent layer connecting customer data, brand guidelines, creative assets, and performance history so agents operate grounded in how the business works.
• Real-time data infrastructure. Low-latency personalization API, sub-second audience evaluation, ML-powered optimization and recommendations, event pipeline processing millions of events daily.

The Role

Founding Product Manager for Agentic Personalization: Hightouch's third major pillar and biggest new investment. You define the product, the sequencing, and the go-to-market.

• Customer discovery. Deep engagement with product and growth teams at enterprise companies to understand how they personalize web and mobile today, and what would earn their trust in an agent-driven approach. Translate into a roadmap tied to revenue milestones.
• Define the agent experience. What agents do autonomously (segments, variants, experiments, traffic allocation) vs. what needs human approval (production launches, brand-sensitive content). Design the feedback loop that builds trust over time.
• Scale the GTM. Co-own pipeline, join customer calls, shape competitive positioning, partner with CS on implementation playbooks.

High-visibility role working directly with founders and executive team.

What We're Looking For

• Zero-to-one track record. Products you built from nothing to meaningful revenue, ideally at a B2B SaaS company. Domain matters less than evidence you can find product-market fit under uncertainty.
• Technical depth. Can evaluate architectural tradeoffs, debate infrastructure decisions with senior engineers, and reason about things like batch vs. real-time and caching strategies at the level of product decisions.
• Commercial instinct. Think in terms of pipeline, conversion, and expansion. Build roadmaps that map to revenue targets.
• Speed. Small team, high autonomy, tight cycles. We use AI heavily in our own workflows.
• Cross-functional trust. This spans engineering, design, sales, CS, and leadership. You drive clarity from ambiguity.

We are looking for talented, intellectually curious, and motivated individuals who are interested in tackling the problems above. This is a senior role, but we focus on impact and potential for growth more than years of experience. The salary range for this position is $240,000 - $320,000 USD per year, which is location independent in accordance with our remote-first policy. We also offer meaningful equity compensation in the form of ISO options, and offer early exercise and a 10 year post-termination exercise window.

About the Role

The Web Engineering team owns our public website (hightouch.com), which is the face of our company and brand and one of the most important ways that customers and prospects learn about Hightouch. We’re looking for a talented senior web engineer who is passionate about crafting exceptional experiences and cares deeply about the story that we tell customers. As Hightouch continues to scale rapidly, you will play a pivotal role in shaping how we position and promote our new product offerings, including cutting edge advancements in marketing AI and ML.

In this role, you’ll partner closely with our marketing and design teams to build, iterate, and maintain our marketing website. Key responsibilities include:

• Lead the design, scoping, and implementation of marketing website projects such as new feature pages, blog revamps, and more
• Integrate CMS-powered interfaces (we use Sanity) that allow marketers to update content independently
• Build and maintain our design system to ensure we can continue to build with high velocity and high standards for UX
• Develop engaging animations that enhance product storytelling using technologies such as Motion and GSAP
• Exercise strong design judgment and attention to detail to ensure that every aspect of our site reinforces our brand identity

About you

• 5+ years of experience in software/web engineering, ideally with strong proficiency in React, TypeScript, CSS, and Next.js
• Demonstrated ability to build production-ready web applications in a fast-paced environment
• Excellent design taste and obsessive attention to detail
• Proven experience with code-based animations (e.g., using Motion or GSAP) or experience collaborating with contractors on creative implementations
• Ability to work autonomously, lead and coach other engineers, and collaborate effectively with marketing, design, and data teams
• Deep understanding of SEO principles and best practices, especially around performance, accessibility, and search optimization

We are looking for talented, intellectually curious, and motivated individuals who are interested in tackling the problems above. This is a senior role, but we focus on impact and potential for growth more than years of experience. The salary range for this position is $170,000 - $220,000 USD per year, which is location independent in accordance with our remote-first policy. We also offer meaningful equity compensation in the form of ISO options, and offer early exercise and a 10 year post-termination exercise window.

Interview Process

Our interview process focuses on evaluating fit for the most important dimensions of the role: cross-functional collaboration, a strong sense of design and user experience, and experience building production frontend components.

• Intro Call [30m]: Introductory call with either a member of our recruiting team or the hiring manager to get to know each other and see if the role could be a good mutual fit.
• Technical Screen [45m]: Work with one of our frontend engineers to build a simple component using React and TypeScript.
• Past Projects Deep Dive [45m]: Walk through 2-3 previous projects (personal or professional) with your interviewer while highlighting big wins, challenges, and key decisions.
• Technical Interview [90m]: Work with the interviewer to design, architect, and implement a complex frontend component.
• Hiring Manager Interview [30m]: Chat with hiring manager about past experiences and future operating preferences to assess fit on company values and operating principles.

We have limited inbound applications to one application per candidate. You will be auto-rejected if you apply to multiple roles. Please only apply to the position you are most qualified for.

About the Role

We're looking for a front-end focused full stack product engineer to join our fast-growing engineering team, where you’ll help develop delightful and intuitive interfaces that enable non-technical users to easily engage with advanced features.

At Hightouch, full stack engineers work closely with design, product managers, solutions engineers, and customers to understand problems and build polished and intuitive features.

On a typical day, you will be:

• Architecting and building features end-to-end for a new generation of marketing and growth products
• Collaborating closely with customers and cross-functional partners - designers, product managers - to understand real-world behavior
• Working alongside ML engineers to surface insights that empower marketers to make confident decisions

Our ideal engineer is someone who has proven experience working closely with product teams to build user interfaces from zero to one. You should be able to independently lead and architect complex frontend systems, and be proficient in building CRUD applications and with a solid understanding of backend API design and data models.

Our frontend is built in React and Typescript, and our backend is also built on Typescript with some elements of Go.

The salary range for this role is $180,000 - $270,000 USD per year, fully remote and location-independent. We also offer meaningful equity compensation in the form of ISO options, and offer early exercise and a 10 year post-termination exercise window.

Interview Process

• Recruiter Screen [30m]: Introductory call with our recruiting team to get to know each other and see if the role could be a good mutual fit.
• Frontend Technical Screen [60m]: You will work together with one of our frontend engineers to build a component that is used commonly throughout the Hightouch app. 
• Full Stack Interview [90m]: This interview is a design-focused session where you'll work collaboratively with the interviewer to think through real product and engineering problems.
• Hiring Manager Interview [30m]: Chat with the hiring manager about past experiences and future operating preferences to assess fit on company values and operating principles.

We have limited inbound applications to one application per candidate. You will be auto-rejected if you apply to multiple roles. Please only apply to the position you are most qualified for.

#LI-DNI

About the Opportunity 

Managing data is a core element of operating the world’s most inspiring Virtual Power Plant (VPP). (You can’t fact check vibes.) Data Engineering is the team that brings it all together. From millions of internet-connected devices that use, store, and distribute power to all the clients that use those devices to manage grid load, Data Engineering makes it possible. We love data, translation, observability, and quality. 

From schema design to data transformation, from privacy and security to compliance, Data Engineering covers a lot of range. It’s a tight-knit team of engineers dedicated to excellence, quality, high-throughput communication, and deep collaboration. Client experience is at the focus of our efforts, but we also serve other internal customers with high-value data and information. 

The Role

You will integrate with the Data Engineering team, collaborating as a full-fledged member of the team, not doing some intern project on the side. You are joining us during the busiest season and will get a full glimpse of what it is like to work on a dedicated team of professionals who care about one another and are dedicated to each other’s success.

• Collaborate closely across teams to help build and maintain EnergyHub’s core data products
• Pair with and receive mentorship from brilliant engineers from a variety of backgrounds, gaining wisdom from their unique experiences and talents
• Deepen your knowledge of modern data engineering tools like Snowflake, dbt, Airflow, and Sigma
• Work in an agile environment, deepening your understanding of translating business and client needs into excellent products
• Contribute to mission-critical projects across the data lifecycle through
  • Feature development
  • Data platform enhancement
  • Bug fixes (identification & resolution)
  • Automations & efficiency improvements
  • Pair programming and code review (providing & receiving feedback)
  • Documentation
  • Monitoring and alerting
• Improve monitoring, troubleshooting, and resolution processes to improve our processes
• Monitor cloud costs across our toolset to maintain efficiency and justify expenses
• Build the absolutely coolest stuff of your career (so far)
• Help your hiring manager sustainably manage their usage of adjectives and adverbs
• Other duties as assigned

About you

• First and foremost, you have to sincerely love data and all the things it can enable and empower
• Current enrollment in (or immediate graduate of) an undergraduate or graduate program in or related to data engineering
• Proficiency in SQL and Python and perhaps even dbt and Sigma experience, with an awesome ability to bring sanity and clarity to syntactic complexity
  • Nice to have: Snowflake Data Warehousing Workshop (Badge 1)
  • Nice to have: dbt Fundamentals
• Some monitoring and dashboarding experience (e.g. Snowflake, Sigma, Datadog)
• A clear understanding of the difference between OLAP and OLTP RDBMS systems and when to recommend one over the other
• Knowledge of a variety of file structures such as CSV, JSON, and Parquet, and when one might be preferable over others
• Drive and dedication for quality and performance in everything you touch and thirst for learning new techniques
• Clear communication (written and verbal), capable collaboration (in-office and online), and cultivated curiosity
• Warmth and welcoming for your collaborators, colleagues, and cohort, with an urge to help them succeed and pay their generosity forward to others
• Above-average appreciation for alliteration
• Well-grounded stance on AI adoption that makes you a stronger, more capable engineer

Please note that sponsorship of new applicants for employment authorization, or any other immigration-related support, is not available for this position at this time.

Why work for EnergyHub?

• Collaborate with outstanding people: Our employees work hard, do great work, and enjoy collaborating and learning from each other.
• Gain well rounded experience: EnergyHub offers a diverse and dynamic environment where you will get the chance to work directly with executives and develop expertise across multiple areas of the business.
• Work with the latest technologies: You'll gain exposure to a broad spectrum of IoT, SaaS and machine learning obstacles, including distributed fault-tolerance, device control optimization, and process modeling to support scalable interaction with disparate downstream APIs.
• Be part of something important: Help create the future of how energy is produced and consumed. Make a positive impact on our climate.

About EnergyHub

EnergyHub empowers utilities and their customers to create a clean, distributed energy future. We help consumers turn their smart thermostats, EVs, batteries, and other products into virtual power plants that keep the grid stable and enable higher penetration of solar and wind power.

To learn more, visit energyhub.com.

EnergyHub is an Equal Opportunity Employer

EOE, Including Disability/Vets. Reasonable accommodations are available for individuals with disabilities throughout the application process. If you are a person with a disability needing assistance with the application process, please contact accommodations.apply@energyhub.net.

In connection with your application, we collect information that identifies, reasonably relates to or describes you ("Personal Information"). The categories of Personal Information that we may collect include your name, government-issued identification number(s), email address, mailing address, other contact information, emergency contact information, employment history, educational history, and demographic information. We collect and use those categories of Personal Information about you for human resources and other business management purposes, including identifying and evaluating you as a candidate for potential or future employment or future positions, recordkeeping in relation to recruiting and hiring, conducting analytics, and ensuring compliance with applicable legal requirements and Company policies. By submitting your application, you acknowledge that we may retain some of the personal data that you provide in your application for our internal operations such as managing our recruitment system and ensuring that we comply with labor laws and regulations even after we have made our employment decision.

Notice To Third Party Agencies:

EnergyHub understands the value of professional recruiting services. However, we are not accepting resumes from recruiters or employment agencies for this position. In the event we receive a resume or candidate referral for this position from a third-party recruiter or agency without a previously signed agreement, we reserve the right to pursue and hire those candidate(s) without any financial obligation to you.

About the Opportunity 

As a Software Engineering Intern on our growing EV team, you will design and develop frontend or backend services that support residential EV programs, while maintaining a strong security posture across our infrastructure. Working closely with leading EV
providers such as Tesla and GM, you will improve grid flexibility and enhance the value of our programs.

Partnering with other engineers, you will build new services and products in EnergyHub’s EV platform, engaging homeowners and OEMs on initiatives that unlock the full potential of EVs and deliver meaningful grid impact.

This role can encompass several areas including data monitoring, internal tooling and reporting, integrations with EV OEMs or other third party services, and new features to improve the quality and breadth of our EV solutions. Ideally, we would love our intern to be with us from July to December.

Main Responsibilities

• Develop Kotlin-based or Ruby on Rails-based solutions, working towards well-structured, high-quality code with automated testing
• Develop front-end solutions supporting EV drivers and/or utilities in React
• Collaborate cross-functionally with product managers, internal engineering teams, and stakeholders to contribute to high-impact projects.
• Work within real-world constraints to develop creative solutions that work towards platform flexibility and scalability.
• Contribute to best practices for CI/CD, data modeling, and iterative development (e.g., PoCs, rapid prototyping, milestone-based execution).
• Actively participate in a strong technical culture of respect, open communication, and collaboration.
• Write and maintain technical documentation
• Drive and embody the “Dev Owns Quality” culture
• Other duties as assigned

Key Skills and Experience

• Eager to learn new technologies and enthusiastic to be a full-stack engineer with a focus on application development, integrations and internal tooling
• Experience developing the full SDLC
• Experience with Java, Kotlin and/or Ruby on Rails
• Experience with React
• Strong problem-solving skills, capable of balancing multiple use cases while enabling flexibility 
• Strong communication skills, with an emphasis on cross-functional collaboration
  and working on challenging technical problems

Preferred Skills and Experience

• Extensive data engineering experience is a huge plus, including addressing data quality issues, building data pipelines, monitoring and alerting
• Experience with relational databases and SQL is a plus
• Experience with S3, MongoDB, AWS, DBT, Snowflake a plus
• Experience with web services and RESTful APIs
• Familiarity with CI/CD pipelines and modern software development practices
• Proven ability to deliver high-quality, iterative value while maintaining execution speed
• Knowledge of modern security best practices, authentication/authorization frameworks, and system/API hardening

Please note that sponsorship of new applicants for employment authorization, or any other immigration-related support, is not available for this position at this time.

Why work for EnergyHub?

• Collaborate with outstanding people: Our employees work hard, do great work, and enjoy collaborating and learning from each other.
• Gain well rounded experience: EnergyHub offers a diverse and dynamic environment where you will get the chance to work directly with executives and develop expertise across multiple areas of the business.
• Work with the latest technologies: You'll gain exposure to a broad spectrum of IoT, SaaS and machine learning obstacles, including distributed fault-tolerance, device control optimization, and process modeling to support scalable interaction with disparate downstream APIs.
• Be part of something important: Help create the future of how energy is produced and consumed. Make a positive impact on our climate.

About EnergyHub

EnergyHub empowers utilities and their customers to create a clean, distributed energy future. We help consumers turn their smart thermostats, EVs, batteries, and other products into virtual power plants that keep the grid stable and enable higher penetration of solar and wind power.

To learn more, visit energyhub.com.

EnergyHub is an Equal Opportunity Employer

EOE, Including Disability/Vets. Reasonable accommodations are available for individuals with disabilities throughout the application process. If you are a person with a disability needing assistance with the application process, please contact accommodations.apply@energyhub.net.

In connection with your application, we collect information that identifies, reasonably relates to or describes you ("Personal Information"). The categories of Personal Information that we may collect include your name, government-issued identification number(s), email address, mailing address, other contact information, emergency contact information, employment history, educational history, and demographic information. We collect and use those categories of Personal Information about you for human resources and other business management purposes, including identifying and evaluating you as a candidate for potential or future employment or future positions, recordkeeping in relation to recruiting and hiring, conducting analytics, and ensuring compliance with applicable legal requirements and Company policies. By submitting your application, you acknowledge that we may retain some of the personal data that you provide in your application for our internal operations such as managing our recruitment system and ensuring that we comply with labor laws and regulations even after we have made our employment decision.

Notice To Third Party Agencies:

EnergyHub understands the value of professional recruiting services. However, we are not accepting resumes from recruiters or employment agencies for this position. In the event we receive a resume or candidate referral for this position from a third-party recruiter or agency without a previously signed agreement, we reserve the right to pursue and hire those candidate(s) without any financial obligation to you.

Come Work With Us:

Metropolitan Commercial Bank (“MCB” or the “Bank”) is a New York City–based, full-service commercial bank providing tailored banking solutions to businesses, institutions, and individuals. Founded in 1999, MCB operates banking centers in Manhattan and Boro Park, Brooklyn, within New York City, as well as in Great Neck on Long Island, New York, and Lakewood, New Jersey. The Bank recently expanded to Miami, Florida with their newest Brickell banking center.

Metropolitan Commercial Bank offers a comprehensive suite of commercial, business, and personal banking products and services to small businesses, middle-market and corporate enterprises, private and public institutions, municipalities, and local government entities.

The Bank has earned national recognition for its financial performance, innovation, and strategic growth. The Bank was named one of Newsweek’s Best Regional Banks in 2024 and 2025. Additionally, MCB recently received Editor’s Choice recognition at the Banking Tech Awards USA for Digital Onboarding & Omnichannel Banking and in 2026, the Bank earned Great Place To Work certification and received the Web Award Standard of Excellence for MCBankNY.com.

We are a client-focused organization that values technological innovation and excellence. A strong technical mindset, AI fluency, and adaptive skills are essential for our employees to effectively contribute to our mission and drive our success. We foster human–AI teaming and strong governance to ensure technology is used responsibly and in alignment with Bank policies and procedures. For more information about the Bank, please visit the Bank’s website at MCBankNY.com.

Position Summary:

Metropolitan Commercial Bank (the “Bank”) is seeking a VP-level AI/ML Engineer to deploy AI solutions at enterprise scale, with a strong emphasis on Large Language Model (LLM) applications and modern MLOps & AIOps practices. This role sits at the intersection of data science and software engineering, reporting to the manager of the IT Application Development and Support team and working closely with the Chief Artificial Intelligence Officer, transforming innovative AI prototypes into robust, scalable production systems. The AI/ML Engineer will lead the deployment of high-impact AI capabilities (e.g., generative AI systems, personalization engines, automation tools) and ensure scalable AI platforms that deliver real-world value. The role also includes designing, constructing, and maintaining the Bank’s AIOps solution, with Snowflake as the primary ML platform (e.g., Snowpark Python, UDFs/UDTFs, Tasks/Streams, and Snowflake-native ML).

Standard 4-day in-office requirement, 1 day remote (of your choosing)

Essential Functions & Responsibilities 

Production Architecture & AIOps

• Establish and enforce architecture standards for production AI systems, including data pipelines, model serving infrastructure, and real-time inference services.
• Implement AIOps/MLOps pipelines for CI/CD of ML models, model governance, monitoring, and lifecycle management.
• Design and maintain scalable software applications with integrated AI/ML capabilities.
• Develop software architecture and design patterns to ensure performance and scalability.
• Write clean, maintainable code in general-purpose programming languages (Python, Java, C, C++, Go).
• Implement and manage data pipelines for preprocessing and transforming data for AI/ML models.
• Integrate AI/ML models into production environments and optimize for reliability and scalability.
• Apply Site Reliability Engineering (SRE) principles and implement monitoring and alerting solutions.
• Conduct code reviews and provide technical guidance to junior developers.
• Stay current with advancements in software engineering and AI/ML technologies.
• Adhere to agile and lean software development best practices.
• Thoroughly document all developed models and processes according to relevant policies and standards.
• Support the production environment by either resolving technical or functional issues, in line with the procedures defined by the Bank.

Cross-Functional Collaboration

• Partner with data scientists, AI scientists, product managers, data engineers, DevOps, and business stakeholders to operationalize AI algorithms.
• Mentor or train teams and coordinate between research-oriented AI scientists and engineering teams to continuously improve models with production feedback.

Scaling & Performance

• Ensure AI solutions perform at scale, handling thousands of daily inferences with low latency and high reliability.
• Optimize model serving using techniques like model compression, caching, and hardware acceleration.

Implement robust monitoring and alerting for model performance to detect and address degradation (e.g., drift, latency issues).

Qualifications & Skills:

• 6+ years of experience
• Master’s or PhD in a relevant field (Computer Science, Software Engineering,  Machine Learning, Data Science, Statistics, etc.) is strongly preferred, especially with research or thesis work related to AI/ML
• LLM & GenAI Mastery: Expert in building and deploying LLM-based applications using RAG, prompt engineering, and vector databases. Skilled in LLMOps tools (LangChain, LlamaIndex) and fine-tuning models for enterprise use, including agent-based architectures.
• MLOps & Cloud Infrastructure: Proficient in cloud ML platforms (AWS, GCP, Azure) and MLOps workflows. Uses Docker, Kubernetes, and IaC tools (Terraform, CloudFormation) for scalable deployments. Experienced in CI/CD, real-time inference, GPU optimization, and ML observability (Prometheus, Grafana, MLflow).
• Full-Stack Development: Capable of building end-to-end AI solutions, from front-end (React) to back-end APIs (Flask, FastAPI, Node.js). Skilled in integrating ML models with databases (SQL, NoSQL) and delivering seamless user experiences through robust software engineering.
• Proficient in Python (pandas, scikit-learn), deep learning (PyTorch/TensorFlow/Keras), NLP/LLMs, LangChain, embeddings/vector search, and classic ML.
• Experienced with Snowflake-native ML (Snowpark Python, UDFs/UDTFs, Tasks/Streams).
• Competent in data engineering (SQL, ETL/pipelines, Spark/PySpark) and handling large structured/unstructured datasets.
• Strong understanding of AI/ML algorithms, application architecture, and design patterns.
• Excellent problem-solving, analytical, communication, and collaboration skills.

Preferred Qualifications & Skills

• Financial services domain experience (fraud risk, AML, underwriting, or commercial/treasury analytics).
• Hands-on experience with Snowflake ML/Snowpark (Python), Tasks/Streams, secure external functions, and feature management/registry tooling.
• Familiarity with fairness toolkits, XAI frameworks, and preparing models for validation, audit, or regulatory exams.
• Knowledge of SR 23‑4 (third‑party risk), NYC Local Law 144 (AEDT), NYDFS Part 500 (cyber).
• Ability to work in a constantly evolving environment.
• Excellent written and verbal communication skills.
• Strong listening and teaching abilities.
• Demonstrated analytical, troubleshooting, and problem-solving skills.
• Quick learner of new technologies.
• Self-directed with strong technology and communication skills.
• Ability to synthesize multiple sources of information and understand the broader operational context of the Bank.
• Collaborative team player with a practical and creative approach in a dynamic work environment.
• Ability to handle ambiguity, multitask, and adapt quickly to changing priorities.

Potential Salary: $130,000 - $200,000 annually

This salary range reflects base wages and does not include benefits, bonus, or incentive pay. Salary bands are purposefully wide ranging to encompass the different factors considered in determining where a candidate falls in the range, including but not limited to, seniority, performance, experience, education, and any other legitimate, non-discriminatory factor permitted by law. Final offer amounts are determined by multiple factors including candidate experience and expertise and may vary from the amounts listed here.

Metropolitan Commercial Bank provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

About This Role

Lead applied ML engineering on Scale's Applied ML team, powering data infrastructure for leading agentic LLMs (ChatGPT, Gemini, Llama). You will build scalable multi-agent systems to validate agentic reasoning and behaviors, scale human expertise, and drive research into real-world agent reliability failures despite strong benchmarks, shipping production fixes.

Ideal for exceptional engineers with deep research rigor and a relentless focus on practical, high-impact systems. You will iterate rapidly with data, leverage AI tools to accelerate development, and collaborate tightly across engineering, product, and research.

If you excel at turning frontier agent research into reliable deployed systems, we want to hear from you.

You will:

• Build and deploy multi-agent systems for agentic reasoning validation
• Develop pipelines to detect errors and scale human judgment
• Combine classical ML, LLMs, and multi-agent techniques for reliability
• Lead research into agent failure modes and ship fixes
• Use AI tools to speed prototyping and iteration
• Build data-driven evaluations and deploy rapid improvements
• Integrate systems into Scale's platform

Ideally You’ll Have: 

• PhD or MSc in Computer Science, Mathematics, Statistics, or related field
• 3+ years shipping scaled production ML systems
• Demonstrated real-world impact
• Mastery of PyTorch, TensorFlow, JAX, or scikit-learn
• Deep expertise in agentic LLMs and multi-agent systems
• Strong software engineering and microservices (AWS/GCP)
• Rapid, data-driven iteration
• Proficiency using AI tools to accelerate work
• Strong research depth with practical bias
• Excellent cross-functional communication

Nice to Have: 

• Experience prototyping agent evaluation/reliability systems
• Human-in-the-loop or annotation pipeline work
• Open-source contributions in agents, evaluation, or alignment
• Publications on agent reliability (NeurIPS, ICML, ICLR)

The goal of a Senior Machine Learning Engineer at Scale is to leverage techniques in the fields of generative AI, computer vision, reinforcement learning, and agentic AI to improve Scale's products and customer experience in production environments. Our machine learning engineers take advantage of robust internal infrastructure and  unique access to massive datasets to deliver improvements to our customers.

Our Public Sector Machine Learning team is focused on deploying cutting-edge models to mission-critical government systems through products like Donovan and Thunderforge. Our work spans multiple modalities, with a strong focus on both large language models and computer vision. On the LLM side, we are developing agentic systems that help solve complex operational and planning challenges for government partners. This includes building agent frameworks that integrate with custom retrieval pipelines and production APIs, as well as evaluation tools to benchmark and refine agent behavior. We're also advancing research in areas like reinforcement learning for agentic LLMs, with successful deployment into real-world operational environments. On the computer vision front, we're training advanced models to increase labeling throughput and automate perception tasks. Our efforts include building large-scale fine-tuning pipelines, training models across multiple modalities, and developing generalizable vision foundation models to support a wide range of defense applications.

You will:

• Take state of the art models developed internally and from the community, use them in production to solve problems for our customers and taskers
• Improve and maintain production models through retraining, hyperparameter tuning, and architectural updates, while preserving core performance characteristics
• Collaborate with product and research teams to identify and prototype ML-driven product enhancements, including for upcoming product lines
• Work with massive datasets to develop both generic models as well as fine tune models for specific products
• Build scalable machine learning infrastructure to automate and optimize our ML services
• Serve as a cross-functional representative and advocate for machine learning techniques across engineering and product organizations
• Be comfortable learning new technologies quickly and managing multiple priorities in a fast-paced environment
• Comfortable with light travel (approximately 10%) for customer interaction and team needs
• This role will require an active security clearance or the ability to obtain a security clearance

Ideally You’d Have:

• Extensive experience with GenAI, Agentic AI, natural language processing, deep learning and deep reinforcement learning, or computer vision in a production environment
• Solid background in algorithms, data structures, and object-oriented programming
• Strong programing skills in Python, experience in Tensorflow or PyTorch

Nice to Haves:

• Graduate degree in Computer Science, Machine Learning or Artificial Intelligence specialization
• Experience working with cloud platforms (eg. AWS or GCP) and deploying machine learning models in cloud environments
• Experience with computer vision, generative AI models, large language models, or agentic systems
• Familiarity with ML evaluation frameworks and agentic model design

Senior Machine Learning Engineer - Model Evaluations, Public Sector

The Public Sector ML team at Scale deploys advanced AI systems—including LLMs, agentic models, and multimodal pipelines—into mission-critical government environments. We build evaluation frameworks that ensure these models operate reliably, safely, and effectively under real-world constraints. As an ML Engineer, you will design, implement, and scale automated evaluation pipelines that help customers trust and operationalize advanced AI systems across defense, intelligence, and federal missions.

You will:

• Develop and maintain automated evaluation pipelines for ML models across functional, performance, robustness, and safety metrics, including LLM-judge–based evaluations.
• Design test datasets and benchmarks to measure generalization, bias, explainability, and failure modes.
• Build evaluation frameworks for LLM agents, including infrastructure for scenario-based and environment-based testing.
• Conduct comparative analyses of model architectures, training procedures, and evaluation outcomes.
• Implement tools for continuous monitoring, regression testing, and quality assurance for ML systems.
• Design and execute stress tests and red-teaming workflows to uncover vulnerabilities and edge cases.
• Collaborate with operations teams and subject matter experts to produce high-quality evaluation datasets.
• Comfortable with light travel (approximately 10%) for customer interaction and team needs.

This role will require an active security clearance or the ability to obtain a security clearance.

Ideally you’d have:

• Experience in computer vision, deep learning, reinforcement learning, or NLP in production settings.
• Strong programming skills in Python; experience with TensorFlow or PyTorch.
• Background in algorithms, data structures, and object-oriented programming.
• Experience with LLM pipelines, simulation environments, or automated evaluation systems.
• Ability to convert research insights into measurable evaluation criteria.

Nice to haves:

• Graduate degree in CS, ML, or AI.
• Cloud experience (AWS, GCP) and model deployment experience.
• Experience with LLM evaluation, CV robustness, or RL validation.
• Knowledge of interpretability, adversarial robustness, or AI safety frameworks.
• Familiarity with ML evaluation frameworks and agentic model design.
• Experience in regulated, classified, or mission-critical ML domains.

AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 9 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent investment from Meta, we are doubling down on building out state of the art post-training algorithms to reach the performance necessary for complex agents in enterprises around the world. 

The Enterprise ML Research Lab works on the front lines of this AI revolution. We are working on an arsenal of proprietary research and resources that serve all of our enterprise clients. As an ML Sys Research Engineer, you’ll work on building out the algorithms for our next-gen Agent RL training platform, support large scale training, and research and integrate state-of-the-art technologies to optimize our ML system. Your customer will be other MLREs and AAIs on the Enterprise AI team who are taking the training algorithms and applying them to client use-cases ranging from next-generation AI cybersecurity firewall LLMs to training foundation healthtech search models. If you are excited about shaping the future of the modern AI movement, we would love to hear from you!

You will: 

• Build, profile and optimize our training and inference framework.
• Post-train state of the art models, developed both internally and from the community, to define stable post-training recipes for our enterprise engagements. 
• Collaborate with ML teams to accelerate their research and development, and enable them to develop the next generation of models and data curation..
• Create a next-gen agent training algorithm for multi-agent/multi-tool rollouts.

Ideally you’d have:

• At least 1-3 years of LLM training in a production environment
• Passionate about system optimization
• Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
• Ability to demonstrate know-how on how to operate the architecture of the modern GPU cluster
• Experience with multi-node LLM training and inference
• Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc. 
• Strong written and verbal communication skills to operate in a cross functional team environment.
• PhD or Masters in Computer Science or a related field

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.

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)

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

Scale plays a vital role in the development of AI applications. Our customer base is growing exponentially, and you will be on the front lines, ensuring that the world's most innovative companies become passionate, lifelong Scale customers.

Solutions Engineers partner closely with AEs, Product, and MLEs to lead prospective customers through pre-sales, delivering customized demos and pilots to secure the “technical win”. Solutions Engineers scope customer technical requirements and develop an actionable SOW. They will work closely with the delivery team to help with initial implementation. Solutions Engineers are relentlessly curious about customer needs and pain points. They employ their expert Scale product knowledge and GenAI knowledge to design solutions that best address these needs. Solutions Engineers are strong relationship builders, great project managers, and provide technical expertise.

You will:

• Partner with Scale AEs on the customer journey, delivering tailored demos and prototypes according to the customer's requirements.
• Develop technical domain expertise in Generative AI / large language model applications for Enterprise use cases, including customers in financial services, insurance, SaaS, and similar enterprises.
• Be accountable for securing the “technical win” by unblocking technical challenges
• Interact with customers daily to understand their needs and design solutions to better serve them.
• Design and develop “Scopes of Work” by breaking down customer challenges into a project plan
• Work closely with forward-deployed Software and Machine learning Engineers to develop agents in the initial post-sales stage
• Work with AEs and PMs to identify customer-specific feature requests.
• Drive strategic initiatives to improve the efficiency and effectiveness of the Solution Engineering team.

Ideally, you'd have:

• Strong engineering background with prior experience working with clients in a pre or post-sales capacity to realize business goals.
• Prior experience developing with Python, Java and/or other web development languages.
• Experience working in enterprise SaaS, cloud tech, finance, fintech or similar industries in a technical capacity with end-customer engagement.
• A track record as a self-starter, motivated to independently unblock technical issues in the field with the customer, away from the mothership.
• 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:

• GenAI Experience
• Forward deployed engineering experience
• Machine Learning Experience

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.

About the General Agents Team

The General Agents team, part of Scale’s Enterprise organization, builds robust general agents for customer use cases and applications. The team sits at the intersection of frontier agent development and real-world deployment, translating state-of-the-art reasoning and agentic capabilities into reliable, production-grade systems that drive real economic value. Our agents are scalable systems built around recurring enterprise problem domains, with a strong emphasis on generalization, extensibility, and deployment across many customers.

About the Role

As a Senior/Staff Machine Learning Engineer (MLE) on the General Agents team, you’ll play a critical role in designing, building, and deploying production-ready AI agents that solve high-impact enterprise problems. You will work across the full agent lifecycle—from model and system design to evaluation, deployment, and iteration—bridging cutting-edge agentic techniques with the constraints and requirements of real customer environments.

You will:

• Design and implement end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases.
• Build scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints.
• Develop evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings.
• Collaborate closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs.
• Productionize frontier agent techniques (e.g., planning, multi-step reasoning and tool-use, multi-agent patterns) into maintainable, observable systems.
• Own deployment, monitoring, and iteration of agent systems, including failure analysis and continuous improvement based on real-world usage.
• Contribute to technical direction and architectural decisions for general agent development best practices and methods, with increasing scope and leadership at the Staff level.

Ideally you’d have:

• 5+ years of experience building and deploying machine learning or AI systems for real-world, production use cases.
• Strong engineering fundamentals, supported by a Bachelor’s and/or Master’s degree in Computer Science, Machine Learning, AI, or equivalent practical experience.
• Deep understanding of modern LLMs, prompt-, context-, and system-level optimization, and agentic system design.
• Proven proficiency in Python, including writing production-quality, testable, and maintainable code.
• Experience building systems that integrate models with external tools, APIs, databases, and services.
• Ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints.
• Strong communication skills and comfort working in customer-facing or cross-functional environments.

Nice-to-haves:

• Hands-on experience building AI agents using modern generative AI stacks (OpenAI APIs, commercial or open-source LLMs).
• Experience with agent frameworks, orchestration layers, or workflow systems (e.g., tool calling, planners, multi-agent setups).
• Familiarity with evaluation, monitoring, and observability for LLM-powered systems in production.
• Experience deploying ML systems in cloud environments and operating them at scale.
• Experience fine-tuning or adapting foundation models using methods like supervised fine-tuning (SFT), reinforcement learning with verifiable rewards (RLVR), and low-rank adaptation (LoRA) to improve agent performance on domain-specific tasks.
• Interest in shaping the future of general-purpose enterprise agents and their real-world impact.

AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 9 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent investment from Meta, we are doubling down on building out state of the art post-training algorithms to reach the performance necessary for complex agents in enterprises around the world. 

The Enterprise ML Research Lab works on the front lines of this AI revolution. We are working on an arsenal of proprietary research, tools, and resources that serve all of our enterprise clients. As a Staff Agent Post-Training MLRE, you will build out our next-gen Agent RL training platform. You’ll build out the platform that will train best-in-class Agents that achieve state of the art results on real enterprise use-cases. 

You’ll integrate cutting edge research into our training stack, enabling MLREs on the Enterprise AI team to deploy use-cases ranging from next-generation AI cybersecurity firewall LLMs to training foundation healthtech search models. If you are excited about shaping the future of the modern GenAI movement, we would love to hear from you!

You will: 

• Train state of the art models, developed both internally and from the community, to deploy to our enterprise customers. 
• Research cutting edge algorithms to integrate directly into our training stack.
• Design solutions that enable complex multi-agent systems to directly learn from both process + outcome based rewards.

Ideally you’d have:

• 5+ years of LLM training in a production environment
• Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
• Publications in top conferences such as NEURIPS, ICLR, or ICML within the last two years
• PhD or Masters in Computer Science or a related field

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

AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 9 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent investment from Meta, we are doubling down on building out state of the art post-training algorithms to reach the performance necessary for complex agents in enterprises around the world. 

The Enterprise ML Research Lab works on the front lines of this AI revolution. We are working on an arsenal of proprietary research, tools, and resources that serve all of our enterprise clients. As MLRE on the Data Foundation team, you’ll work on cutting edge research to define the data flywheel that makes the whole machine move. This includes research around synthetic environments from task definitions, building agents for trace analysis, and contributing to a cutting edge framework that automatically hill-climbs agent-building from an eval set. This will involve creating best-in-class Agents that achieve state of the art results through a combination of post-training + agent-building algorithms.

If you are excited about shaping the future of the modern GenAI movement, we would love to hear from you!

You will: 

• Build synthetic data pipelines to generate enterprise environments to use for RL post-training
• Create agents to convert traces from production into actionable insights to use to improve agents
• Contribute to our agent building product which can construct other agents using coding agents + proprietary algorithms
• Train state of the art models, developed both internally and from the community, to deploy to our enterprise customers. 

Ideally you’d have:

• 3+ years of building with LLMs in a production environment
• Clear experiences with constructing high quality data to use to improve an LLM/Agent
• Publications in top conferences such as NEURIPS, ICLR, or ICML within the last two years
• PhD or Masters in Computer Science or a related field

Scale plays a critical role in powering the next generation of AI applications across the enterprise. As our customer base grows exponentially, Principal Solutions Engineers operate as senior technical leaders on the front lines—shaping how the world’s most innovative companies adopt, deploy, and scale AI with Scale.

Principal Solutions Engineers are trusted advisors to both customers and internal stakeholders, owning the most complex and high-impact opportunities. They partner closely with Sales, Product, and Engineering leadership to drive technical strategy, secure executive alignment, and deliver differentiated solutions that win and expand strategic accounts. Beyond individual deals, they influence product direction, define best practices, and elevate the capabilities of the broader Solutions Engineering organization.

You will:

• Own technical strategy and execution for the company’s most complex, high-value enterprise opportunities, acting as the ultimate arbiter of the “technical win”
• Serve as a trusted advisor to executive stakeholders (CTOs, Heads of AI, Engineering leaders), guiding long-term AI/LLM adoption strategies
• Architect and lead end-to-end solution design, from discovery through pilots and productionization, across ambiguous and evolving customer requirements
• Drive cross-functional alignment across Sales, Product, and Engineering to unblock critical technical and business challenges
• Define and shape large, multi-phase Scopes of Work, translating ambiguous customer problems into scalable, production-ready solutions
• Lead high-impact customer engagements, including executive presentations, technical deep dives, and workshops
• Influence Scale’s product roadmap by synthesizing customer insights and advocating for high-leverage feature development
• Mentor and elevate other Solutions Engineers, establishing best practices across demos, pilots, and technical sales strategy
• Drive org-level initiatives that improve win rates, shorten sales cycles, and increase the scalability of the Solutions Engineering function

Ideally, you'd have:

• Deep technical background with significant experience in customer-facing engineering roles (pre-sales, forward-deployed, or post-sales) at enterprise scale
• Proven track record of owning and closing complex, multi-stakeholder technical deals with Fortune 500 or equivalent enterprises
• Strong systems thinking and architecture experience across cloud, data, and AI/ML systems
• Hands-on experience with programming languages such as Python, Java, or similar, with the ability to prototype and validate solutions when needed
• Experience operating as a senior technical advisor to executive stakeholders, with strong presence and credibility
• Demonstrated ability to navigate ambiguity, influence without authority, and drive alignment across diverse teams
• Exceptional communication skills, with the ability to translate between business, product, and technical audiences
• High degree of ownership, intellectual curiosity, and bias for action in fast-paced, ambiguous environments

Nice to haves:

• Deep experience with Generative AI / LLM applications in enterprise environments
• Background in forward-deployed engineering or building production-grade ML systems
• Experience in regulated industries such as financial services, insurance, or healthcare

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

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).

Scale AI is seeking a highly technical and strategic Staff / Senior Staff Machine Learning Engineer to act as the Tech Lead (TL) for our next generation of deep research agents for the Enterprise. This high-impact role will drive the technical direction and oversight for Deep Research Agent Development, translating cutting-edge research in Generative AI, Large Language Models (LLMs), and Agentic Frameworks into robust, scalable, and high-impact production systems that enhance enterprise operations, analytics, and core efficiency.

The ideal candidate thrives in a fast-paced environment, has a passion for both deep technical work and mentoring, and is capable of setting a long-term technical strategy for a critical domain while maintaining a strong, hands-on delivery focus.

Responsibilities

Technical Leadership & Vision

• Set the Technical Roadmap: Define and own the technical strategy, architecture, and roadmap for Deep Research Agents for the Enterprise, ensuring alignment with Scale AI’s overall AI strategy and business goals.
• Drive Breakthrough Research to Production: Lead the end-to-end development, from initial research to production deployment, to landing on customer impact, with a focus on integrating diverse data modalities.
• Core Agent Capabilities Development:
• Advanced Knowledge Retrieval: Architect and implement state-of-the-art retrieval systems to ensure the agents provide accurate and comprehensive answers from public and proprietary data sources from enterprises.
• Data analysis: Design and champion the development of data analysis agents that accurately translate complex natural language queries into executable SQL/code against diverse enterprise data schemas.
• Multimodal Intelligence: Lead the integration of Multimodal AI capabilities to process and extract structured information from visual documents, tables, and forms, enriching the agent's knowledge base.
• Architecture & Design: Design and champion highly scalable, reliable, and low-latency infrastructure and frameworks for building, orchestrating, and evaluating multi-agent systems at enterprise scale.
• Technical Excellence: Serve as the technical authority for the team, leading design reviews, defining ML engineering best practices, and ensuring code quality, security, and operational excellence for all agent systems.

Team Leadership & Mentorship

• Lead and Mentor: Technically lead and mentor a team of Machine Learning Engineers and Research Scientists, fostering a culture of innovation, rigorous engineering, rapid iteration, and technical depth.
• Recruiting & Growth: Partner with management to hire, onboard, and grow top-tier talent, helping to shape the long-term structure and capabilities of the team.
• Cross-Functional Influence: Collaborate effectively with Product Managers, Data Scientists, and other engineering/science teams to translate ambiguous, high-level business problems into concrete, executable technical specifications and impactful agent solutions.

Basic Qualifications

• Bachelor's degree in Computer Science, Electrical Engineering, a related field, or equivalent practical experience.
• 8+ years of experience in software development, with at least 6 years focused on Machine Learning, Deep Learning, or Applied Research in a production environment.
• 2+ years of experience in a formal or informal Technical Leadership role (Team Lead, Tech Lead) with a focus on setting technical direction for a domain.
• Deep expertise in Generative AI and Large Language Models (LLMs).
• Demonstrated experience designing, building, and deploying AI Agents or complex Agentic systems in production at scale.
• Experience with large-scale distributed systems and real-time data processing.

Preferred Qualifications

• Advanced degree (Master's or Ph.D.) in Computer Science, Machine Learning, or a related quantitative field.
• Demonstrated experience designing and deploying production-grade Text-to-SQL systems, including handling complex schema linking and query optimization.
• Practical experience with Multimodal AI, specifically integrating OCR and vision-language models for document intelligence and structured data extraction from images/forms.
• Proven experience in one or more relevant deep research areas: Reinforcement Learning (RL), Reasoning and Planning, Agentic Systems.
• Experience with vector databases and advanced retrieval techniques.
• A track record of publishing research papers in top-tier ML/AI conferences (e.g., NeurIPS, ICML, ICLR, KDD).
• Excellent written and verbal communication skills, with the ability to articulate complex technical vision to executive stakeholders and technical peers.
• Experience driving cross-team technical initiatives that have delivered significant business impact.

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.

AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 9 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent investment from Meta, we are doubling down on building out state of the art post-training algorithms to reach the performance necessary for complex agents in enterprises around the world. 

The Enterprise ML Research Lab works on the front lines of this AI revolution. We are working on an arsenal of proprietary research, tools, and resources that serve all of our enterprise clients. As an Agent MLRE, you will be working on applying our Agent RL Training + Building algorithms to real life enterprise datasets across our clients + benchmarks. This will involve creating best-in-class Agents that achieve state of the art results through a combination of post-training + agent-building algorithms.

If you are excited about shaping the future of the modern GenAI movement, we would love to hear from you!

You will: 

• Train state of the art models, developed both internally and from the community, to deploy to our enterprise customers. 
• Research cutting edge algorithms to integrate directly into our training stack.
• Build agents that leverage our proprietary agent-building algorithms to automatically hill climb datasets – including defining highly performant tools, multi-agent systems, and complex rewards.

Ideally you’d have:

• 1-3 years of building with LLMs in a production environment
• Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
• Publications in top conferences such as NEURIPS, ICLR, or ICML within the last two years
• PhD or Masters in Computer Science or a related field

Scale’s ML platform (RLXF) team builds our internal distributed framework for large language model training and inference. The platform has been powering MLEs, researchers, data scientists and operators for fast and automatic training and evaluation of LLM's, as well as evaluation of data quality.

Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely across Scale’s ML teams and researchers to build the foundation platform that supports all our ML research and development. You will be building and optimizing the platform to enable our next generation of LLM training, inference and data curation.

If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you!

You will:

• Build, profile and optimize our training and inference framework
• Collaborate with ML teams to accelerate their research and development and enable them to develop the next generation of models and data curation
• Research and integrate state-of-the-art technologies to optimize our ML system

Ideally you’d have:

• Strong excitement about system optimization
• Experience with multi-node LLM training and inference
• Experience with developing large-scale distributed ML systems
• Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc. 
• Strong written and verbal communication skills and the ability to operate in a cross functional team environment

Nice to haves:

• Demonstrated expertise in post-training methods &/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.

Job Description: Distinguished Engineer, Enterprise AI 

About Scale AI

Our mission is to develop reliable AI systems for the world's most important decisions. The Enterprise AI business delivers performant, reliable, and production-grade AI applications to enterprises across all industries by providing Generative AI expertise and infrastructure.

The Role

We’re seeking a Distinguished Engineer - a hands-on technical leader operating at the VP level - to shape the vision and technical roadmap of our core AI/ML infrastructure that powers enterprise AI applications. Reporting directly to the SVP of Engineering, Enterprise AI, this individual will drive long-term technical direction for our Scale GenerativeAI Platform (SGP), influence architectural decisions across the company, and partner closely with engineering and product leaders to bring advanced AI capabilities to enterprise customers.

You’ll serve as a cross-organizational thought leader - setting standards for technical excellence, mentoring senior engineers, and ensuring our systems and models meet the demands of global-scale deployment. This is a rare opportunity to influence both foundational AI infrastructure and the enterprise AI applications built on top of it.

Responsibilities

• Define and drive the technical strategy for Scale’s AI/ML infrastructure and SGP platform, balancing short and long-term investments.
• Partner with senior engineering and product leadership to ensure scalable, secure, and performant enterprise AI systems.
• Lead architecture and design reviews across multiple teams, ensuring technical consistency and innovation.
• Serve as a trusted advisor and mentor to principal engineers and technical leads across the organization.
• Evaluate and integrate emerging technologies in AI, distributed systems, and data infrastructure to keep Scale at the frontier of innovation.
• Represent Scale externally in the AI community - through speaking engagements, partnerships, and thought leadership.
• Drive technical execution and accountability for critical cross-functional initiatives that advance Scale’s enterprise AI capabilities.

Qualifications

• 15+ years of experience as a technical software engineering leader.
• Proven record of technical leadership at AI-native companies, hyperscalers, or equivalent high-scale environments.
• Deep technical expertise in AI/ML infrastructure, knowledge of ML models/algorithm design/implementation and their application to real-world problems; experience with GenAI preferred.
• Demonstrated success in setting technical vision and leading cross-organizational initiatives with measurable business impact.
• Experience influencing and mentoring engineering teams in complex, matrixed environments.
• Ability to communicate and collaborate effectively to create a shared sense of vision or purpose cross-team and cross-functionality.
• Advanced degree in Computer Science, Engineering, or related field preferred but not required.

Culture & Impact

At Scale, we believe that AI should amplify human potential - and our engineering culture reflects that belief. Our teams operate at the intersection of innovation, rigor, and impact, solving some of the hardest problems in AI infrastructure and deployment.

The Distinguished Engineer will play a key role in shaping how AI systems are built, deployed, and governed within enterprise environments. This role represents the highest bar of technical excellence at Scale - a trusted voice in setting direction, enabling innovation, and ensuring that our technology scales responsibly and effectively to meet the evolving needs of our customers.

You’ll have the opportunity to influence company-wide strategy, contribute to industry-leading work in generative AI infrastructure, and mentor the next generation of engineering talent pushing the boundaries of what’s possible with AI.

At Scale AI, we help leading enterprises turn AI from a promising capability into reliable systems that improve real workflows and deliver measurable business value. We are hiring a Senior Forward Deployed Data Scientist / Engineer to work directly with customers on ambiguous, high-impact problems at the intersection of data science, product development, and AI deployment.

This is not a traditional analytics role. On this team, data scientists do the core statistical and modeling work, but they also build real tools and products: evaluation explorers, operator workflows, decision-support systems, experimentation surfaces, and customer-specific AI/data applications that get used in production. In many cases, the data scientist builds the first usable version of the solution, proves value quickly, and helps drive it into a durable product or platform capability.

The right candidate is strong in first-principles problem solving, rigorous measurement, and technical execution. They know how to define metrics, design experiments, diagnose failures, and build systems that people actually use. They are also comfortable using modern AI-assisted development tools to prototype and iterate quickly without sacrificing reliability, observability, or judgment. Python and SQL matter in this role, but as execution fluency in service of building better products and making better decisions.

What you’ll do

• Partner directly with enterprise customers to understand workflows, operational pain points, constraints, and success criteria
• Turn ambiguous business and product problems into measurable solutions with clear metrics, technical designs, and deployment plans
• Design and build internal and customer-facing data products, including evaluation tools, workflow applications, decision-support systems, and thin product layers on top of data/ML systems
• Build end-to-end solutions across data ingestion, transformation, experimentation, statistical modeling, deployment, monitoring, and iteration
• Design evaluation frameworks, benchmarks, and feedback loops for ML/LLM systems, human-in-the-loop workflows, and model-assisted operations
• Apply rigorous statistical thinking to experimentation, causal inference, metric design, forecasting, segmentation, diagnostics, and performance measurement
• Use AI-assisted development workflows to accelerate prototyping and product iteration, while maintaining strong engineering discipline
• Diagnose failure modes across data quality, model behavior, retrieval, workflow design, and user experience, and drive fixes into production
• Act as the voice of the customer to Product, Engineering, and Data Science, using field learnings to shape roadmap and platform capabilities

What we’re looking for

• 5+ years of experience in data science, machine learning, quantitative engineering, or another highly analytical technical role
• Proven track record of shipping data, ML, or AI systems that delivered measurable business or product impact
• Exceptional ability to structure ambiguous problems, define the right success metrics, and translate them into executable technical plans
• Strong foundation in statistics, experimentation, causal reasoning, and measurement
• Experience building tools or products, not just analyses — for example internal workflow tools, evaluation systems, operator-facing products, experimentation platforms, or customer-specific applications
• Hands-on fluency in Python, SQL, and modern data/AI tooling; able to inspect data, prototype quickly, debug deeply, and productionize solutions that work
• Comfort using AI-assisted coding and development workflows to move from idea to usable product quickly
• Strong communication and stakeholder management skills; able to work effectively with customers, engineers, product teams, and executives
• High ownership and bias toward shipping in fast-moving environments with incomplete information

Preferred qualifications

• Experience in a forward deployed, solutions, consulting, or other client-facing technical role
• Experience designing evaluation frameworks for LLMs, retrieval systems, agentic workflows, or other AI-enabled products
• Experience with large-scale data processing and distributed systems such as Spark, Ray, or Airflow
• Experience with cloud infrastructure and modern data platforms such as AWS, GCP, Snowflake, or BigQuery
• Experience building lightweight applications, APIs, internal tools, or workflow software on top of data/ML systems
• Familiarity with marketplace experimentation, causal inference, forecasting, optimization, or advanced statistical modeling
• Strong product instinct and the judgment to know when the right answer is a model, an experiment, a tool, or a workflow redesign

What success looks like

Success in this role means taking a messy, high-stakes customer problem and turning it into a deployed system that is actually used. Sometimes that system is a model. Sometimes it is an evaluation framework. Sometimes it is an operator-facing tool or a lightweight data product that changes how decisions get made. In all cases, success is defined by measurable impact, rigorous evaluation, and reliable execution.

Role Summary

Scale is accelerating the development of AI across the world's most important organizations. The Forward Deployed PM is the person who makes enterprise deployments succeed from the product side — embedded with customers, shaping real production outcomes, and translating messy operational reality into high-signal input for the core platform.

This is not a roadmap PM, a CSM, or a solutions engineer. The FDPM owns product outcomes inside a portfolio of enterprise accounts: earning deep trust with senior customer stakeholders, driving deployments to production, identifying where the product is the bottleneck vs. execution, and feeding durable, specific signal back to the platform team. The best FDPMs have shipped products into large organizations and can tell the difference between a customer's stated request, their actual problem, and what the platform should do.

What You'll Do

• Own product outcomes for our highest impact enterprise accounts. You are the accountable PM for product delivery and customer value.
• Build trusted advisory relationships with VP/C-level buyers and senior technical leads. Become the reliable escalation point on the Scale side before issues surface externally.
• Drive deployments from contract to production. You have done this before for large organizations. You know what 'production-ready' actually means and you close the gap.
• Translate real customer workflows, friction, and priorities into high-signal product input for core platform teams. Not requirements docs — specific, evidence-backed product perspective with tradeoffs articulated.
• Distinguish where the product is the constraint from where execution, integration, or change management is. Drive each type of blocker to resolution through the right channel.
• Partner closely with FDEs so the account runs as a coordinated team, not a set of parallel workstreams.
• Identify and develop expansion opportunities grounded in demonstrated customer value 
• Represent customer reality to platform and leadership with enough credibility that it changes decisions.

What We're Looking For

Core Competencies

• Enterprise deployment ownership: has driven AI/ML or complex software products from contract to production at scale, with real integration, real security constraints, real change management.
• Product judgment in the field: translates customer reality into product direction, not just feature requests; can say no credibly and explain why.
• Customer trust-building: earns and maintains trust with demanding senior stakeholders over multi-quarter engagements; manages conflict without eroding the relationship.
• Technical translation: bridges the gap between customer engineering teams and Scale platform engineers; does not relay messages, adds interpretation.
• Operating in ambiguity: creates structure where there is none; makes decisions with incomplete information; does not wait for fully defined requirements.
• Clear, precise communication: adjusts for audience (exec vs. engineer vs. PM); writes and speaks at the right level of detail; does not over-explain or under-explain.

Must-Have Qualifications

• 6+ years in product management, technical program management, or a customer-facing product ownership role.
• Demonstrated record of shipping products or platform capabilities into large organizations — not pilots or proofs-of-concept. You can name the customer, the scale, the integration, and what production actually looked like.
• Extreme ownership and follow-through: closes loops without reminders, drives outcomes across teams without formal authority, holds self and others to commitments.
• Comfortable operating with senior customer stakeholders (VP/C-level) and senior engineers simultaneously; credible presence in high-stakes rooms on both sides.
• Strong judgment under pressure: makes good calls quickly with incomplete information, knows when to escalate vs. resolve, does not generate thrash.
• Technical fluency sufficient to hold a real conversation with a platform engineer about architectural tradeoffs — not deep coding, but genuine comprehension of how systems work in production.

Preferred Qualifications

• Direct experience with AI/ML platform products — data labeling, RLHF, fine-tuning workflows, or model evaluation pipelines.
• Experience in a startup or early-stage environment where process didn't exist and had to be created.
• Prior forward-deployed or embedded role (consulting, field PM, solutions engineering with ownership).

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.