Engineering Manager, ML and Data Products

Engineering Manager

We are looking for an Engineering Manager to join the Data Products team at Strava, a team at the core of Strava's AI strategy, responsible for turning Strava's unique community and activity data into reliable, reusable, enriched datasets powering user experiences at scale. This is a technical management role leading a growing team of Machine Learning Engineers, Data Engineers and Data Scientists. You'll be responsible for hands-on technical contributions, driving execution and setting technical strategy as well as coaching and growth of your team. You'll balance innovative machine learning models with product impact via iterative development to translate durable, high-quality capabilities into athlete experiences at scale across our many product verticals.

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

What You'll Do:

• Build for a Well Loved Consumer Product: Work 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. Contribute hands on to the solutions we deliver in product.

• Lead a High-Impact Data + ML Team: Manage, mentor, and grow 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: Drive the roadmap for Data Products (both the models, datasets and systems) owning from initial model prototyping to production deployment, scaling, and optimization

• Drive Innovation in ML for Fitness: Guide your team in designing and developing novel models algorithms and dataset for unique fitness, routing and athlete insights

• Building cross-functional partnerships: Develop strong relationships and effectively communicate with many cross-functional partners in product and engineering to identify highest leverage opportunities across product verticals

• Championing team culture: Be 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

• Build from a rich dataset: 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

You Will Be Successful Here By:

• Treating Data Products as Products: Bringing engineering rigor versioning, contracts, SLAs, monitoring, and deprecation paths to data artifacts and ML insights that product teams depend on. You don't ship a pipeline; you ship a capability.

• Leading as an Owner: 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.

• Building for Leverage: 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.

• Collaborating Across Disciplines: Working fluidly with ML engineers, data engineers, data scientists, and product managers to align on artifact semantics, evaluation standards, and consumption patterns.

• Raising the Standard: Helping establish best practices for data product development, access patterns, and operational health and mentoring teammates at all levels to do the same.

• Being passionate about the work you are doing and contributing positively to Strava's inclusive and collaborative team culture and values.

What You'll Bring to the Team:

• 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 broken 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 and serving in backend service development on cloud environments (AWS preferred), using Python.

• Interested in production ML model operational excellence and best practices, including scalable ML architecture, serving optimization and dataset versioning.

• Excellent communication and collaboration skills, with the ability to influence and align stakeholders across multiple engineering and product teams.