Engineering Team Lead

MasteryPrep•Louisiana, LA

3d•Remote

About The Position

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. 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.

Requirements

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.

Nice To Haves

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.

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.