About the job
Baseten's engineers want to work in an AI-first way. What's missing isn't enthusiasm — it's the platform underneath it. Today everyone assembles their own agent config, context files, and MCP servers, so the good patterns stay trapped in individual setups instead of becoming defaults everyone inherits.
You'll build that platform: the agent configurations tuned to our monorepo, the context and tooling layer that makes agents competent in our codebase, the evals that tell us which approaches actually work, and the rollout mechanics that get a new engineer productive with agents in week one.
You are not here to mandate how engineers use AI — you're here to make the good path the easy path. Success looks like teams adopting what you build because it beats what they'd cobble together themselves, not because a policy requires it. Platform engineer, not AI evangelist. Ship infrastructure, measure it, kill what doesn't work, let adoption be the referee.
The playbook for AI-first SDLC doesn't exist at any company yet. You'll write ours.
Responsibilities
- Own the internal AI developer platform end to end — architecture, build, rollout, operation, measurement.
- Evaluate and integrate third-party AI coding tools (Claude Code, Cursor, Codex, and whatever ships next quarter), and build the context layer that makes them work against our monorepo.
- Build frameworks that let other engineers create their own agents without deep LLM expertise.
- Establish the evaluation practice for AI-assisted development at Baseten, and use it to drive investment decisions.
- Drive adoption through developer experience — good defaults, clear docs, low friction — not mandate.
- Embed with teams to find where AI genuinely unblocks them, then generalize those wins into platform capabilities.
- Own the safety layer: permissions, secrets handling, audit trails, cost management.
Qualifications
Minimum
- Have 4+ years of relevant industry experience building and enabling AI native SDLC
- Strong proficiency in Python and/or Go, building tools other engineers depend on daily.
- Hands-on experience with LLMs and agent frameworks — tool calling, MCP, context management, orchestration, failure handling. You've shipped something agentic that real people used, not just prototyped.
- Deep personal fluency with AI coding tools and well-formed opinions about where they break down.
- Platform mindset: you build for adoption and self-service, treat internal engineers as customers, and would rather ship a good default than write a style guide.
- Developer tooling, CI/CD, and Kubernetes/Docker fundamentals.
- Comfort with ambiguity — this space invalidates its own best practices every few months.
- Excellent written communication. Much of your leverage is docs, templates, and examples that scale beyond conversations you're in.
Preferred
No preferred qualifications listed.