软件工程师 - AI开发者生产力
Software Engineer - AI Developer Productivity
关于Baseten
Baseten为全球最具活力的AI公司提供关键的推理支持，包括Cursor、Notion、OpenEvidence、Abridge、Clay、Gamma和Writer。通过结合应用AI研究、灵活的基础架构和无缝的开发者工具，我们使处于AI前沿的公司能够将最先进的模型投入生产。我们正在快速成长，并最近完成了15亿美元的F轮融资，由Altimeter Capital、Conviction Partners和Spark Capital领投。加入我们，帮助构建工程师们用来交付AI产品的平台。
职位描述
Baseten的工程师希望以AI优先的方式工作。缺乏的不是热情——而是支撑这一切的平台。如今每个人都在自行组装自己的代理配置、上下文文件和MCP服务器，因此好的模式只能停留在个人设置中，而不是成为所有人都能继承的默认选项。
你将构建这个平台：针对我们的单体仓库优化的代理配置、使代理在我们的代码库中具备能力的上下文和工具层、告诉我们哪些方法真正有效的评估系统，以及让新工程师在第一周就能使用代理的部署机制。
你不是来这里规定工程师如何使用AI的——你是为了让正确的路径变得简单。成功意味着团队因为你的成果比他们自己拼凑的更好而采用它，而不是因为政策要求。是平台工程师，不是AI传教士。交付基础设施，衡量它，淘汰无效的部分，让采用情况成为裁判。
目前还没有任何公司拥有AI优先的SDLC指南。你将撰写我们的指南。
示例项目
代理基础层——代码库级别的上下文基础设施，使代理在我们的代码库中具备能力（CLAUDE.md/AGENTS.md http://CLAUDE.md/AGENTS.md 约定、架构和领域上下文，以及保持代码变动时准确性的工具）。内部MCP服务器，为代理提供对CI、可观测性、事件工具、部署状态和文档的范围访问。共享技能、子代理和钩子，用于编码Baseten的工作流程。沙盒环境，让代理可以安全地构建和测试。
黄金路径——带有已配置并运行的AI工具的项目模板和入门流程。自助式基础设施，让团队无需你作为瓶颈即可构建自己的代理。网关、认证、成本控制和审计日志，用于内部模型访问。
反馈循环——评估框架，回答“是否”
查看英文原文
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 https://www.baseten.co/blog/announcing-our-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
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.
EXAMPLE INITIATIVES
Agent substrate — Repo-level context infrastructure that makes agents competent in our codebase (CLAUDE.md/AGENTS.md http://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
- (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
- 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).