THE ROLEBaseten'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.
WHAT YOU'LL BUILDAgent substrate - Repo-level context infrastructure that makes agents competent in our codebase (CLAUDE.md/AGENTS.md conventions, architecture and domain context, and the tooling to keep it accurate as code moves). Internal MCP servers giving agents scoped access to CI, observability, incident tooling, deployment state, and docs. Shared skills, subagents, and hooks that encode Baseten workflows. Sandboxed environments where agents can build and test safely.
The golden path - Project templates and onboarding that ship with AI tooling configured and working. Self-serve infrastructure so teams build their own agents without you as the bottleneck. Gateway, auth, cost controls, and audit logging for internal model access.
The feedback loop - Eval harnesses that answer "is this config better than that one" against real Baseten tasks, not vibes. Instrumentation of AI tool usage and its downstream effects on cycle time, review latency, and change failure rate. Honest reporting, including on what you built that didn't pan out.
Agents in the SDLC - Automation where agents earn their keep: PR review triage, test gap-filling, incident context assembly, migrations and refactors, codebase Q&A. Integrating agents into CI/CD with guardrails that make it trustworthy.
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.
REQUIREMENTS- 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.
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.
