Hybrid Senior Machine Learning Platform Engineer

at Strava

• Job title: Senior Machine Learning Platform Engineer
• Job type: Full Time
• Experience level: Senior
• Salary: $180,000 - $200,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, Cloud, Numpy, Pandas, Python, PyTorch, Spark, SQL, Tensorflow, Terraform
• Location requirements: Hybrid, San Francisco, United States

About the role

• 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.

Responsibilities

• 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

• 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

• Offers Equity