Engineering Team Lead

Description

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.
  
  

• 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