Manage and grow the ML team through clear expectations, regular feedback, and strong coaching.
Define quarterly ML goals and success metrics, establish baselines, and build a visible “scoreboard” for progress.
Drive end-to-end execution on key ML initiatives: planning, evaluation, iteration, rollout, and impact measurement.
Build a repeatable model evaluation and experimentation process (quality, performance, reliability), including rollout criteria.
Partner with Product and Engineering leaders to align roadmaps, clarify ownership, and ensure ML work is shippable and predictable.
Identify gaps in our current ML strategy, propose concrete bets, and translate direction into actionable plans.
Help hire thoughtfully as the team grows, without prematurely scaling into a large org.

8–10+ years overall experience in applied ML, with a track record of shipping ML and LLM systems into production.
4+ years people management experience, including leading 1:1s, giving feedback, and handling performance.
Strong technical judgment in applied ML (not primarily ML infra/MLOps), including ability to define and run rigorous evaluations.
Excellent metric sense and analytical thinking: can translate ambiguous goals into measurable outcomes and make tradeoffs.
Strong cross-functional collaboration skills with Product, Engineering, and Design.
Clear written communication: can write crisp strategy docs, decision memos, and goal docs.
Bonus: speech/audio, linguistics, or multimodal experience.

Applied ML Manager

Key skills