Senior Machine Learning Platform Engineer

Strava

Senior Machine Learning Platform Engineer at Strava developing AI/ML systems for fitness applications. Driving innovative solutions using machine learning models and large datasets to enhance user experience.

Posted 4/12/2026full-timeSan Francisco • California • 🇺🇸 United StatesSenior💰 $180,000 - $200,000 per yearWebsite

Tech Stack

Tools & technologies

AWSCloudNumpyPandasPythonPyTorchSparkSQLTensorflowTerraform

About the role

Key responsibilities & impact

Own End to End Systems: Drive key projects to power AI/ML at Strava end-to-end from gathering stakeholders requirements to ground up developer to driving adoption and optimizing the experience
Interact with a Rich and Large Dataset: Explore and help leverage Strava’s extensive unique fitness and geo datasets from millions of users to extract actionable insights, inform product decisions, and optimize existing features
Contribute to a Well Loved Consumer Product: Work at the intersection of AI and fitness to help launch and maintain product experiences that will be used by tens of millions of active people worldwide

Requirements

What you’ll need

Have worked on complex, ambiguous platform challenges and broken them down into manageable tasks with both strategies and tactical execution.
Demonstrated technical leadership in leading projects and the ability to mentor and grow early-career team members.
Have demonstrated strong interpersonal and communication skills, and a collaborative approach to drive business impact across teams.
Have worked with a variety of MLOps tools that fulfill different ML needs (like FastAPI, LitServe, Metaflow, MLflow, Kubeflow, Feast)
Are experienced in production ML model operational excellence and best practices, like automated model retraining, performance monitoring, feature logging, A/B testing
Experience with generative AI technologies around LLM evaluation, vector stores, and agent frameworks.
Have built backend production tools and services on cloud environments like (but not limited to) AWS, using languages Python, Terraform, and other similar technologies.
Have built and worked on data pipelines using large scale data technologies (like Spark, SQL, Snowflake)
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, Sagemaker

Benefits

Comp & perks

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Hard Skills & Tools

MLOpsFastAPILitServeMetaflowMLflowKubeflowFeastPythonTerraformSpark

Soft Skills

technical leadershipmentoringinterpersonal skillscommunication skillscollaborationstrategic executiontactical execution