Staff Machine Learning Engineer

About This Role

Strava is the app for active people. With over 150 million athletes in more than 190 countries, it’s more than tracking workouts—it’s where connection, motivation, and personal bests thrive. No matter your activity, gear, or goals, Strava’s got you covered. Find your crew, crush your milestones, and keep moving forward. Start your journey with Strava today.

We are looking for a Staff Machine Learning Engineer to join the growing AI and Machine Learning team at Strava. This team is responsible for sophisticated machine learning models and systems that power key Strava experiences which provide value to our athletes including personalization, recommendation, search, and trust and safety.

This is a leadership role in the ML team and across Product teams designing, roadmapping and implementing innovative machine learning algorithms. We value full stack ML engineers who are able to work on all parts of an ML pipeline from model building, evaluation, optimizing performance, and ensuring the scalability and reliability of these production models.

We follow a flexible hybrid model that generally translates to around half your time on-site in our San Francisco Office—roughly three days per week. 

You’re excited about this opportunity because you will:

• Build for a Well Loved Consumer Product: Work at the intersection of AI and fitness to launch and optimize product experiences that will be used by tens of millions of active people worldwide
• Own End to End AI Systems: Lead key projects powered by ML on the Strava platform end-to-end, from initial model prototyping to shipping production code to scaling and optimizing inference and deployment
• Shape AI at Strava: Be a leadership voice and mentor on a highly collaborative team with a range of experience levels. Lead across teams to deploy ML solutions in multiple surfaces.
• Innovate in AI for Fitness: Design and develop novel models and methodologies to take on novel problems in that improve athlete experience, including recommendation systems, activity prediction, and personalized insights
• Build from a rich dataset: Explore and use 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:

• Setting AI technical vision: Build, foster and expand the influence of AI here at Strava through understanding and collaboration with partners across teams set technical strategies for delivering impact through AI.
• Driving innovation with Product in mind: Stay up-to-date with the latest research in machine learning, AI, and related fields. Experiment, advocate and get support for innovative techniques to improve existing products or explore new features that result in step function changes to how we build AI .
• Raising the ML standard: Mentor engineers to shape how we do ML at Strava. Drive best practices for model development, deployment, and maintenance and be a go-to source of knowledge of the field.
• Collaborating in and across teams: Build relationships, advocate and connect with cross-org partners and product verticals understand needs, and build systems to bring your technical vision to life.
• Leading as an Owner: Owning your work end-to-end and being accountable for the outcomes in the projects you lead, influencing the ML team, partner teams and landing impact for the business. Ensure the end to end system delivers as expected.
• Analyzing the Data: Work closely with product managers, data scientists, and engineers to find opportunities for applying machine learning to drive impact and enhance Strava’s features and measure impact.
• Being passionate about the work you are doing and contributing positively to Strava’s inclusive and collaborative team culture and values

We’re excited about you because:

• Have worked on complex, ambiguous machine learning problems and broken them down into manageable tasks with both strategies and tactical execution.
• Demonstrated technical leadership in leading large projects and the ability to mentor and grow team members of all levels.
• Have demonstrated strong interpersonal and communication skills, and collaborative approach to drive strategies and drive business impact across teams.
• Have experience building, shipping, and supporting ML models in production at scale
• Have experience with exploratory dat