About Company
Join Strava as a Machine Learning Engineer!
About This Role
Strava is the premier digital community for active individuals with more than 125 million athletes across 190+ countries. Our platform provides a comprehensive view of your active lifestyle, regardless of your location, preferred sport, or device. Everyone has a place on Strava when living an active life.
This position is on our AI and Machine Learning team, which develops the ML models powering core product experiences on Strava. These models enhance personalization, recommendations, growth modeling, activity analysis, search, and more essential elements of the Strava ecosystem.
We're hiring a full-stack ML engineer who will work on every aspect of the ML pipeline: from model prototyping and deployment to building production data pipelines, writing backend production code, and handling production dev ops.
This is a hybrid role based in our San Francisco office. For details on compensation and benefits, please .
Why You’ll Love This Opportunity:
- Collaborate at the intersection of AI and fitness to create products utilized by millions of active people worldwide
- Lead key ML-powered projects from initial prototyping to production
- Join a highly collaborative team with diverse experience levels
- Explore Strava’s vast dataset to discover unique ML solutions
You'll Succeed Here By:
- Being passionate about your work and contributing positively to Strava’s inclusive and collaborative team culture and values
- Working closely with product teams to design, develop, and ship projects
- Owning your work end-to-end and being accountable for the outcomes
- Embracing a growth mindset and proactively learning new skills
- Mentoring other ML engineers on the team
We’re Excited About You Because You:
- Have experience building, deploying, and supporting ML models in production at scale
- Are skilled in exploratory data analysis and model prototyping using Python, R, Scikit-learn, Pandas, Numpy, PyTorch, SageMaker, and more
- Have experience with data pipelines using big data technologies like Spark, Hadoop, EMR, SQL, and Snowflake
- Experienced in production ML model operational excellence, with best practices like automated model retraining, performance monitoring, and feature logging
- Have developed backend cloud-based services in a production environment using languages such as Ruby, Java, Scala, Python, Go, and AWS tools
About Strava
Strava, meaning “strive” in Swedish, reflects our ethos. We are dedicated to connecting athletes with what motivates them, helping them achieve their personal bests. With billions of activity uploads from around the world, we aim to be the record of all athletic activities and provide the technology that makes every effort count.
We create software that enhances the best part of our athletes' days. As committed as we are to unlocking their potential, we are equally dedicated to offering an inclusive workplace where our employees can grow. Backed by Sequoia Capital, Madrone Partners, and Jackson Square Ventures, we are expanding to better serve our global athlete community.
Our culture mirrors our community. We continuously strive to hire and engage diverse teammates from all backgrounds, experiences, and perspectives, knowing we are stronger together. Despite global challenges, we maintain a positive culture and are committed to becoming an antiracist company. Our people-first approach, compassionate leadership, and mission to bring joy and inspiration to athletes make Strava an exciting workplace.
Strava is an equal opportunity employer. We make all employment decisions without regard to race, religion, color, sex, age, national origin, ancestry, sexual orientation, physical handicap, mental disability, medical condition, disability, gender identity or expression, pregnancy, marital status, height, or weight.
We ensure reasonable accommodations for individuals with disabilities during the job application or interview process, to perform essential job functions, and to receive other employment benefits. Please contact us to request accommodation.