Engineering Manager, ML and Data Products

About the role

The role owns the end-to-end strategy and execution for Data Products at Strava, translating community and activity data into reliable, reusable, enriched datasets that power user experiences at scale. You will lead a growing team of Machine Learning Engineers, Data Engineers, and Data Scientists while balancing innovative machine learning models with product impact through iterative development. The position requires hands-on technical contributions, coaching, and growth of your team to deliver durable, high-quality capabilities across Strava's many product verticals. You will build for a well-loved consumer product at the intersection of fitness and geospatial, contributing directly to solutions used by tens of millions of active people worldwide. The role follows a flexible hybrid model requiring more than half your time on-site in the San Francisco office, specifically three days per week.

Key facts

What you'll do

Build for a Well Loved Consumer Product by working at the intersection of fitness and geospatial to launch and optimize product experiences that will be used by tens of millions of active people worldwide, contributing hands-on to the solutions we deliver in product.

Lead a High-Impact Data + ML Team by managing, mentoring, and growing a team of machine learning engineers, data engineers, and data scientists to deliver ML and data-powered experiences to users while fostering a collaborative culture across experience levels.

Own End-to-End Data Products Strategy and Execution for Data Products, driving the roadmap for models, datasets, and systems from initial model prototyping to production deployment, scaling, and optimization.

Drive Innovation in ML for Fitness by guiding your team in designing and developing novel models, algorithms, and datasets for unique fitness, routing, and athlete insights.

Build cross-functional partnerships by developing strong relationships and effectively communicating with many cross-functional partners in product and engineering to identify highest leverage opportunities across product verticals.

Champion team culture by being passionate about developing your people and contributing positively to Strava's inclusive and collaborative culture, fostering an environment where your team can do their best work.

Unlock your curiosity and explore Strava's extensive unique fitness and geo datasets from millions of users to extract actionable insights, inform product decisions, and optimize existing features.

Treat Data Products as Products by bringing engineering rigor, versioning, contracts, SLAs, monitoring, and deprecation paths to data artifacts and ML insights that product teams depend on, ensuring you ship a capability rather than just a pipeline.

Lead as an Owner by taking end-to-end accountability for the reliability and impact of the systems you build, including their correctness in production, their adoption by downstream teams, and the business outcomes they enable.

Build for Leverage by designing platforms and tooling that multiply the output of the broader team, reducing the ML and data engineering expertise required for CUJ teams to ship features on top of data products.

Collaborate Across Disciplines by working fluidly with ML engineers, data engineers, data scientists, and product managers to align on artifact semantics, evaluation standards, and consumption patterns.

Raise the Standard by helping establish best practices for data product development, access patterns, and operational health, and mentoring teammates at all levels to do the same.

Be passionate about the work you are doing and contribute positively to Strava's inclusive and collaborative team culture and values.

Requirements

2 years of experience managing an AI/ML engineering team, with a proven track record of growing engineers and delivering complex technical projects.

Demonstrated track record of solving complex, ambiguous machine learning problems and breaking them down into strategies and tactical execution for teams.

Technical experience building, shipping, and supporting complex ML models in production at scale.

Experience building and maintaining production data pipelines and batch/stream workflows using technologies like Spark, Kafka, Snowflake, or similar.

Hands on coding for model development, including feature engineering, training, evaluation, and inference.

Strong experience with data manipulation and analysis using tools such as SQL, Python, and relevant data science libraries.

Ability to communicate technical concepts to both technical and non-technical stakeholders, translating complex ideas into actionable plans.

Commitment to writing high-quality, maintainable, and testable code, with experience in version control and code review processes.

Practical notes

The role follows a flexible hybrid model that translates to more than half your time on-site in our San Francisco office - three days per week.