The posting, in Baseten's own words
archived Oct 22, 2026ABOUT 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
AI is being built by many companies at once. Model labs train more capable models, hyperscalers provide infrastructure and distribution, developers build new AI products, and a growing set of tools gives agents more to work with. Baseten sits at the center of all of it, as the inference platform that developers and large companies use to run AI in production. We’re hiring a backend/distributed systems engineer to join our Product Platform in following areas: Commercialization, billing, and partnerships Developer productivity and agentic development Observability platform Please come join our growing team to work on exciting problems. A few sample projects and responsibilities will include Design and build scalable telemetry ingest and storage pipelines for metrics, logs, and traces across Baseten’s multi-cloud infrastructure Own and evolve core observability platforms, driving migrations and architectural improvements that improve reliability, reduce cost, and scale with organizational growth Build world class commercialization and billing systems Work on some of the most strategic and exciting partnership integrations Develop internal infrastructure and tooling that improves developer velocity, CI/CD, and deployment safety. Own the internal AI agent platform end to end — architecture, build, rollout, operation, measurement. Own Baseten's testing strategy end to end — define the standards, the tiers, and the tooling that engineering teams build against. Drive adoption through developer experience — good defaults, clear docs, low friction — not mandate. Improve platform reliability through better alerting, incident tooling, and production debugging workflows. Lead technical design for cross-team projects and make sound tradeoffs between speed, quality, and long-term maintainability. Partner with infrastructure, model performance, and product teams to understand what they need to see and ship faster. Raise the engineering bar through code review, mentorship, and clear technical writing.