Baseten is a company that powers mission-critical inference for dynamic AI companies, and they are seeking a full-stack, product-minded engineer to join their Labs team. This role involves building products that help model labs and AI researchers ship and scale their models, with a focus on both backend systems and developer-facing interfaces.
Responsibilities:
- Take meaningful ownership of projects: from API design and backend implementation to frontend surfaces, rollout, and operation
- Build backend services with high reliability and clear SLOs — auth, rate limiting, quotas, metering, and multi-tenant isolation
- Ship developer-facing product surfaces: dashboards, onboarding flows, and self-serve tooling that reduce time-to-value
- Collaborate closely with design, product, and GTM to define and ship what labs and developers actually need
- Drive performance and reliability improvements through profiling, tracing, and load testing
Requirements:
- 4+ years building and operating production software, including at least some full-stack experience (backend-primary is fine, but you're comfortable touching the frontend)
- Demonstrated ability to take initiative and contribute beyond the spec — you think about the 'why' behind what you build
- Strong backend fundamentals: API design, distributed systems, observability, and operational rigor
- Comfort working across the stack: backend services, data pipelines, and user-facing product surfaces
- Strong written communication — clear design docs, effective async collaboration
- Genuine curiosity about the AI/ML infrastructure space; you don't need ML expertise, but you want to understand the ecosystem
- Experience building developer-facing products: APIs, SDKs, CLIs, dashboards, or self-serve workflows
- Experience with API gateways, auth systems, billing/metering infrastructure, or multi-tenant platforms
- Frontend experience (React/TypeScript) or strong product UX instincts for developer tools
- Familiarity with model serving, LLM runtimes, or inference platforms
- Comfort with Kubernetes, distributed scheduling, or service mesh concepts