Primary record

Staff Product Manager, AI/ML

Strava Indexed employerStrava SF
Source-hosted applyChecked 2h ago$250K–$270K/yrFull-Time
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Workplace

hybrid

Employment

Full-Time

Published

Jun 23, 2026

Closes

No date supplied

The role

ABOUT STRAVA Strava is the app for active people. With over 200 million athletes in more than 185 countries, it’s more than tracking workouts—it’s where people make progress together, from new habits to new personal bests. No matter your sport or how you track it, Strava’s got you covered. Find your crew, crush your goals, and make every effort count. Start your journey https://www.strava.com/subscription with Strava today. Our mission is simple: to motivate people to live their best active lives. We believe in the power of movement to connect and drive people forward. ABOUT THIS ROLE Strava's AI/ML team builds the intelligence that shapes what each athlete sees and experiences — from ranking and recommendations to the next generation of generative AI features across the app. As we deepen our investment in AI-powered products, we're looking for a Staff PM to own the strategy for ranking, recommendations, and personalization: a high-impact surface with significant value to unlock. In this role, you'll define the product vision for personalization across Strava's core experiences, partner deeply with ML engineering and data science to turn model capabilities into products athletes love, and contribute to Strava's broader generative AI strategy. You'll operate with a high degree of autonomy, set direction for a domain, and work across Product, Engineering, and Design to bring the most important bets to life. We follow a flexible hybrid model that translates to more than half of your time on-site in our San Francisco office, three days per week. WHAT YOU'LL DO - Define and own the domain strategy and roadmap for ranking, recommendations, and personalization across Strava's core athlete experiences. - Identify and prioritize high-value GenAI and ML product opportunities across the app, and build a pipeline of bets that connect to company-level priorities. - Partner closely with ML engineering and data science to translate model capabilities, evaluation criteria, and tech

Requirements

Department: Department; Team: Product Management