See how candidates think and approach the work this role demands, before the phone screen. We'll build a video challenge from this posting, and you can edit or share it before it goes live.
Baseten is staffing a dedicated product lead for 'Grid,' a model-routing layer, signaling a push beyond single-model inference hosting toward a broader cloud where inference, training, and agent execution run as one platform.
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.
Baseten is building a new cloud for AI. Our ambition is to make inference, training, and agent execution part of one coherent platform, and to shape how the next generation of AI products gets built. Product decisions here reach from the economics of GPU infrastructure to the abstractions developers build on every day.
We’re hiring builders and tastemakers with the ambition to help create that future. You should bring a strong point of view about what should exist, the taste to make complex technology feel simple, and the technical judgment to build it alongside engineers, researchers, and designers. You’ll work directly with customers and forward deployed teams, turn emerging capabilities into new possibilities for developers, and carry an idea from early conviction through to production and adoption.
Build the future of AI model routing, where every task gets the right model, automatically.
Today, developers often have to choose between models that are faster, cheaper, or more capable. But the right model isn't always the same for every request.
Grid is Baseten's intelligent model routing platform, designed to help developers get the best possible results across open and frontier models without manually managing every decision.
We're starting with coding agents, where model choice can have an enormous impact on quality, speed, and cost.
As Product Manager for Grid, you'll work with engineers, researchers, and customers to build a new category of AI infrastructure. You'll help shape how intelligent routing works, how developers control it, and how we prove it's better than choosing a single model.
Own the product roadmap for Grid, from early developer adoption to production deployments and enterprise expansion.
Work with researchers and engineers to improve intelligent model selection, routing policies, fallbacks, and controls.
Build evaluation frameworks that measure real-world outcomes, including task quality, completion time, latency, and total cost.
Shape the developer experience for onboarding, configuration, observability, and routing decisions.
Help customers understand why a model was selected and give them confidence and control over how routing behaves.
Work through technical challenges involving conversation history, tool compatibility, prompt caching, retries, and routing overhead.
Partner with GTM and customers to turn early pilots into sustained adoption and revenue growth.
Experience building and commercializing AI/ML products or developer platforms, with demonstrated adoption or business impact.
A technical background in software engineering, ML engineering, research, or equivalent hands-on experience.
Experience working with researchers or ML engineers on model behavior, evaluation, or learned systems in production.
Strong understanding of AI model quality, latency, cost, and the tradeoffs involved in optimizing across them.
Excellent developer product instincts, with an appreciation for both simple APIs and enterprise-level controls.
Hands-on experience using coding agents and a strong point of view on where automation helps developers and where they need control.
Comfort building emerging products where the technical approach and customer expectations are still evolving.
Experience with LLM gateways, model routing, AI agents, coding harnesses, or multi-model platforms.
Developers can build better AI applications without having to manually choose and optimize every model. Grid consistently delivers measurable improvements in quality, speed, or cost, and becomes a trusted part of customers' production AI stacks.
BENEFITS
Competitive compensation, including meaningful equity
(U.S. only) 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
(U.S. only) 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).
baseten:9b71cb02-d82c-421c-b73b-4b783076409ebasetenThis posting hasn't been probed by our closure verifier yet. Stream C runs on a rolling schedule against postings approaching the close-decision threshold.
See how we measure for definitions, or our corrections log for known issues. Found something wrong? Flag a correction.