Machine Learning Engineer developing and optimizing AI systems at Strava. Work includes building innovative models that enhance fitness experiences for millions of users.
About the role
- Machine Learning Engineer developing AI systems for Strava's fitness platform. Working on personalized recommendations and optimizing user experiences for millions worldwide.
Responsibilities
- 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: Drive 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 strong voice on 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
- Have worked on numerous machine learning problems and broken them down into incremental tasks.
- Have demonstrated solid interpersonal and communication skills, and collaborative approach to drive business impact 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
- For information on benefits, please click here.
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