Machine Learning Engineer

Strava

Machine Learning Engineer developing and optimizing AI systems at Strava. Work includes building innovative models that enhance fitness experiences for millions of users.

Posted 4/12/2026full-timeSan Francisco • California • 🇺🇸 United StatesMid-LevelSenior💰 $160,000 - $180,000 per yearWebsite

Tech Stack

Tools & technologies

AWSCloudGoHadoopJavaNumpyPandasPythonPyTorchRubyScalaSparkSQLTensorflow

About the role

Key responsibilities & impact

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
Craft End to End AI Systems: Contribute to 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: Bring your voice and creativity to a highly collaborative team with a range of experience levels. Work across teams to deploy ML solutions in multiple surfaces and build out our technical ML capabilities.
Innovate in AI for Fitness: Design and develop novel models and methodologies to take on novel problems 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

Requirements

What you’ll need

Have worked on impactful machine learning problems delivering incremental progress towards long-term goals.
Have demonstrated solid interpersonal and communication skills, and collaborate across teams.
Have experience building, shipping, and supporting ML models in production at scale.
Have experience with exploratory data analysis and model prototyping, using languages such as Python or R and tools like Scikit learn, Pandas, Numpy, Pytorch, Tensorflow, and Sagemaker.
Have built and worked on data pipelines using large scale data technologies (like Spark, Hadoop, EMR, SQL, and Snowflake).
Are experienced and interested in production ML model operational excellence and best practices, like automated model retraining, performance monitoring, feature logging, and A/B testing.
Have built backend production services on cloud environments like AWS, using languages like (but not limited to) Python, Ruby, Java, Scala, and Go.

Benefits

Comp & perks

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Hard Skills & Tools

machine learningmodel prototypingdata analysisrecommendation systemsactivity predictiondata pipelinesautomated model retrainingperformance monitoringA/B testingbackend services

Soft Skills

interpersonal skillscommunication skillscollaboration