工程团队负责人
Engineering Team Lead
你将作为亲力亲为的教练型领导者带领一个小团队的产品工程师:亲自编写代码,帮助他人解决问题,并对产品的质量和流程负责。这项工作的核心是设计和运行一个开发流程,其中AI代理承担大量实现工作——同时人类工程师始终对发布的成果负责。你将决定在哪些地方信任代理,在哪些地方需要人类决策,以及如何验证这种差异。
我们如何协作
我们认为AI改变了工程师的工作内容,而不是他们是否重要。代理会编写我们的大部分初稿;工程师定义工作内容,审查每项变更,执行验收测试,并对每个提交和合并给予最终批准。人类始终对所有发布的成果负责。如果你认同这种分工方式——对自动化充满雄心,对验证毫不妥协,你会喜欢这里的工作。
主要职责
- 领导团队。管理、指导并培养产品工程师团队。进行规划,制定产品工程策略,并确保交付可预测。
- 主导AI原生流程。定义工作如何分解为代理可执行的任务:启动提示、验收标准、用户验收测试检查点和完成报告。持续优化有效的方法。
- 保持人类对关键事项的掌控。建立明确的人类决策点——PR审批和合并、提交签名确认和用户验收测试,确保AI输出在落地前始终由负责的工程师验证。
- 发布产品。设计、构建和维护使用AI原生工程方法实现的功能。
- 主导数据和集成。管理我们的数据库架构以及与学校连接的集成——SIS学生名单管道(OneRoster、Clever、ClassLink),确保正确的学生被分配到正确的班级并获得正确的数据。
- 设定质量标准。制定适合服务每年百万名学生的平台的测试标准、CI检查、评审规范和安全实践。
- 测量并改进。跟踪AI辅助开发带来的帮助和问题。淘汰拖慢团队效率的流程;扩大使团队更快更安全的做法。
技能、知识和专长
- 7年以上专业软件工程经验,包括2年以上作为团队领导或工程经理带领工程师的经验,同时保持技术参与度。
- 在生产环境中具备React和TypeScript的全栈深度,同时具备Node.js或其他类似技术的扎实后端经验。
- 真实的实践经验
查看英文原文
You will lead a small team of product engineers as a hands-on player-coach: shipping code yourself, unblocking others, and owning the health of both the product and the process. The core of the job is designing and running a development workflow where AI agents do a large share of the implementation work — and where human engineers remain firmly accountable for what ships. You will decide where agents are trusted, where humans must decide, and how we verify the difference.How We Work
We believe AI changes what engineers do, not whether they matter. Agents write a lot of our first drafts; engineers define the work, review every change, run acceptance testing, and give the final greenlight on every commit and merge. Humans stay accountable for everything that ships. If that division of labor sounds right to you — ambitious about automation, uncompromising about verification — you will like it here.
Key Responsibilities
- Lead the team. Manage, mentor, and grow a team of product engineers. Run planning, set the product engineering strategy, and keep delivery predictable.
- Own the AI-native process. Define how work is decomposed into agent-runnable tasks: kickoff prompts, acceptance criteria, UAT checkpoints, and completion reports. Continuously refine what works.
- Keep humans in charge of what matters. Establish clear human-decision points — PR approval and merge, commit sign-off, and user acceptance testing — so AI output is always verified by an accountable engineer before it lands.
- Ship product. Design, build, and maintain features with AI native engineering.
- Own data and integrations. Steward our database architecture and the integrations that connect us to schools — SIS rostering pipelines (OneRoster, Clever, ClassLink) that get the right students into the right classes with the right data.
- Set the quality bar. Testing standards, CI checks, review discipline, and security practices appropriate for a platform serving a million students a year.
- Measure and improve. Track where AI-assisted development helps and where it hurts. Kill process that slows the team down; scale what makes it faster and safer.
Skills, Knowledge and Expertise
- 7+ years of professional software engineering experience, including 2+ years leading engineers as a team lead or engineering manager while remaining hands-on.
- Strong full-stack depth with React and TypeScript in production, plus solid back-end experience with Node.js or similar.
- Real experience with Google Cloud Platform (Cloud Run, Cloud SQL, Pub/Sub, IAM, or comparable services) and with PostgreSQL — schema design, query performance, migrations.
- Working fluency with AI-assisted development — you have used tools like Claude Code, Cursor, or Copilot on real production work, and you have opinions about where they excel and where they fail.
- A reviewer’s eye: you can read a large volume of code (human- or machine-written) quickly and catch what matters.
- Clear written communication — in an AI-native process, well-written task specs and prompts are engineering artifacts.
- Judgment, pragmatism, and steady ownership under ambiguity.
Preferred Qualifications
· Experience with education technology: SIS/rostering integrations (OneRoster, Clever, ClassLink), LMS ecosystems, or student data privacy (FERPA, COPPA).
- Experience building or evaluating LLM-powered product features — prompt design, evals, guardrails.
- Experience scaling engineering process at a growing company.
Benefits
- $150,000 - $160,000 salary range
- Employee benefits eligibility (health, disability, AD&D, life insurance).
- Matching 401k
- Paid time off
- Generous paid holidays
- Ability to work fully remote
- Company supplied device/laptop
Originally posted on Himalayas