
AI Engineer at Baseten (San Francisco, CA)
Baseten· San Francisco, CA· $175k–$190k·
Role details
Baseten at a glance
AI inference platform for deploying, optimizing, and running machine learning models in production at scale.
Baseten runs trained AI models in production for other companies, handling the GPUs, autoscaling, runtime, and performance tuning so engineering teams get a fast, reliable API without operating the infrastructure themselves. It supports open source, custom, and fine tuned models across managed cloud, hybrid, and self hosted deployments.
$2B+ raised · latest: Series F · $1.5B · June 2026 (valuations of $13B and $11B across two tranches) · backed by Altimeter Capital, Conviction, Spark Capital, Sands Capital
Job description
ABOUT BASETEN
Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.
THE ROLE
Baseten's Compute org is in hyper growth. As it scales, the systems and workflows that keep supply and demand balanced across our GPU fleet need to get more sophisticated, and this role exists to make sure they do. Compute sits at the center of how Baseten allocates, forecasts, and manages the capacity that powers every customer inference request. The team that supports this work, C3, runs on a mix of internal tooling, manual processes, and systems that haven't fully kept pace with the scale of the problem. This role exists to close that gap. You'll design, build, and ship AI-powered workflows that give the Compute and C3 teams real leverage, automating the manual, repetitive, and error-prone parts of the capacity lifecycle so the team can focus on judgment calls that actually need a human. We want someone who can walk in, audit what exists today, identify what's missing or broken, and start shipping fast. You know when to reach for an existing internal tool and when to build something custom in Claude Code. You think two to three steps ahead about how the thing you build today fits into the broader capacity systems architecture tomorrow. And you bring a point of view on our stack, on what we should be building, and on where AI can do something existing tooling simply can't.
RESPONSIBILITIES
- Ship AI-powered workflows for Compute and C3: build the agents and automations that give capacity analysts, ops leads, and engineers real leverage, off-loading manual and repetitive work like data pipeline cleanup, migration troubleshooting, and capacity investigation.
- Get insights in front of the team: turn fleet utilization, allocation, and demand-forecasting data into the dashboards, alerts, and recommendations that C3 and Compute leadership actually act on. A build isn't done until the team is using it.
- Audit the stack and generate your own backlog: Compute runs on a mix of tooling with real overlap and real gaps. Walk in with a point of view, identify what's missing or broken, and prioritize without waiting to be handed a roadmap.
- Ship team-productivity workflows fast: triage asks from Supply, Demand, and C3, find the low-hanging fruit, and build it. A good week looks like an ops lead asking for something Monday and having it live by Wednesday.
- Know when to go custom: not everything belongs in a point-and-click tool. Spin up bespoke AI-powered solutions in Claude Code when the problem calls for it, and make that call with judgment, not default.
- Think in systems, not solutions: every workflow you build has upstream and downstream implications across Supply, Demand, and Engineering. Anticipate them, design for them, and don't create technical debt someone else has to unwind six months later.
- Integrate third-party APIs and internal systems to sync events and information across disparate tools: ensuring data flows reliably between C3, the CRM, and the systems Compute depends on every day.
- Document what you build: if people can't find it, understand it, or trust it, it doesn't matter how well it works.
REQUIREMENTS
- 3+ years of experience in AI/automation engineering, workflow automation, or a technical operations role, ideally at a high-growth, AI-native infrastructure or B2B company
- You've shipped production agents on Vercel. Build custom internal apps and agents on Vercel, with durable workflows and sandboxed execution, so what you ship runs reliably in production instead of as one-off scripts
- Genuine fluency with AI coding assistants and agent tooling (Claude Code, Cursor, Codex, or similar)
- Direct experience with a CRM or system-of-record platform (Salesforce or similar)
- A track record of owning end-to-end AI and automation workflows
- Proficiency with integration and automation platforms (n8n, Zapier, Make, Workato, or similar) and a fundamental understanding of APIs and webhooks
- Experience in high-growth technology companies, ideally in infrastructure, cloud, or operations-heavy environments
NICE TO HAVE
- Familiarity with GPU infrastructure, capacity planning, or fleet management concepts
- Comfort building reporting and alerting on a data warehouse (BigQuery, Databricks) and BI layer (Sigma, Hex) — not as a data engineer, but as a consumer and builder on top of the data layer
- Experience building with agent platforms (Gumloop, n8n, Notion Agents, etc.) and/or agent frameworks (Vercel AI SDK, Claude Agent SDK, Mastra, etc.)
- Experience with high-stakes, operationally complex environments where mistakes have real business impact
- Challenging work and exposure to the operational core of a fast-scaling AI infrastructure company
BENEFITS
- Competitive compensation, including meaningful equity
- 100% coverage of medical, dental, and vision insurance for employee and dependents
- Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
- Paid parental leave
- Fertility and family-building stipend through Carrot
- Company-facilitated 401(k)
- Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.
Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).
Why work at Baseten
- Hard Technical Problems: Engineers work on the most challenging problems in modern infrastructure—model serving, low-level GPU optimization, networking, distributed systems, and observability. This is a high-agency, high-impact engineering environment.
- High Growth Trajectory: The company is experiencing explosive growth (224% headcount increase, active hiring). This offers significant career acceleration and ownership opportunities.
- Strong Engineering Culture: Founded by engineers, for engineers. The culture emphasizes "first-principles thinking across the entire stack" and a "customer-obsessed" mindset. The employer rating on compensation, culture, and work-life balance is rated highly (5.0).
- Top-Tier Team & Investors: The team has strong talent density with hires from Meta, Stripe, Google, NVIDIA, and Databricks. Being backed by top-tier VCs provides stability and a clear long-term vision.
- Hybrid/In-Office: Based in San Francisco with a strong in-person or hybrid culture common for fast-moving infrastructure startups.