Staff Product Manager, AI/ML

About the role

Strava is building the intelligence that shapes what each athlete sees and experiences across the app, and this role owns the strategy for ranking, recommendations, and personalization as high-value surfaces for unlocking value. You will define the product vision for personalization across core athlete journeys and translate complex model capabilities into products that athletes actively love and use. This position requires a high degree of autonomy as you set direction for a critical domain and navigate ambiguity to deliver impactful outcomes. You will partner deeply with ML engineering and data science teams to ensure technical feasibility and meaningful evaluation criteria drive product decisions. The role contributes directly to Strava's broader generative AI strategy while ensuring every bet connects to company-level priorities and athlete needs. You will align cross-functional stakeholders to maintain momentum on complex initiatives and resolve ambiguity without direct authority. Finally, you will help shape the future of AI-powered features across Strava's surfaces, ensuring the athlete experience is consistently improved through thoughtful, measurable product decisions.

Key facts

What you'll do

Define and own the domain strategy and roadmap for ranking, recommendations, and personalization across Strava's core athlete experiences, ensuring alignment with long-term product goals.

Identify and prioritize high-value GenAI and ML product opportunities across the app, building a pipeline of bets that connect to company-level priorities and measurable impact.

Partner closely with ML engineering and data science to translate model capabilities, evaluation criteria, and technical trade-offs into clear, shippable product decisions that athletes can feel.

Build and drive experimentation and measurement frameworks that demonstrably improve relevance, engagement, and meaningful athlete outcomes over time.

Align stakeholders across Product, Engineering, and Design on strategy and priorities, resolving ambiguity and keeping complex initiatives moving efficiently.

Contribute to the broader AI/ML product strategy, including identifying where generative AI can meaningfully improve the athlete experience across Strava's primary and secondary surfaces.

Evaluate model outputs and data pipelines to ensure product decisions are grounded in real-world performance, reliability, and athlete trust.

Champion a user-centric approach by translating technical possibilities into intuitive experiences that lower friction and increase genuine engagement.

Operate with ownership and bias for action, making informed decisions quickly while balancing risk, trade-offs, and cross-team dependencies.

Communicate progress, rationale, and results clearly to both technical and non-technical audiences, ensuring alignment and shared understanding.

Lead discovery efforts to validate assumptions, uncover unmet athlete needs, and surface opportunities that data alone might not reveal.

Establish guardrails and success metrics for new features, ensuring they drive sustainable engagement and positive behavior change.

Collaborate with research teams to assess emerging techniques and determine how they can be productized into reliable, user-facing features.

Influence company-wide AI/ML priorities by sharing insights, learnings, and best practices from the domain to elevate the broader product vision.

Requirements

Substantial product management experience, including meaningful time shipping ML-powered products such as ranking, recommendations, personalization, search, or feeds at scale within consumer applications.

Deep fluency in modern ML and generative AI concepts, including enough familiarity to engage substantively with engineers and data scientists on models, evaluation, and data pipelines.

A track record of owning domain-level strategy end-to-end, demonstrating the ability to define vision, set direction, and see initiatives through execution.

Strong experimentation and measurement instincts, with demonstrated ability to use data to drive measurable improvements in relevance, engagement, and user outcomes.

The ability to align and influence senior stakeholders across functions, bringing clarity to complex, ambiguous problem spaces without relying on direct authority.

A low-ego, partnership-oriented approach to working with technical and design partners, fostering trust and productive collaboration.

Experience productizing emerging or research-stage capabilities into reliable, user-facing features is a preferred background that strengthens execution in this role.

Comfort operating in fast-moving, ambiguous environments while maintaining rigor in decision-making and clear communication.

Practical notes

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