
Applied ML Manager
Speak
full-time
Posted on:
Location Type: Hybrid
Location: San Francisco • California • United States
Visit company websiteExplore more
Salary
💰 $250,000 - $300,000 per year
About the role
- 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.
Requirements
- 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.
Benefits
- Offers Equity 📊 Check your resume score for this job Improve your chances of getting an interview by checking your resume score before you apply. Check Resume Score
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learninglarge language modelsmodel evaluationexperimentation processimpact measurementdata analysisperformance evaluationmetric definitionrigorous evaluationsapplied ML
Soft Skills
people managementcoachingfeedbackanalytical thinkingcross-functional collaborationwritten communicationstrategic thinkinggoal settingperformance handlingclarity in expectations