Strava is looking for a Machine Learning Platform Engineer to develop and expand the platform behind the curtain, to build models of higher quality with less friction, and to ensure models are served with stability and reliability.
Requirements
- 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
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
Other
- Holding empathy and perspective: Work closely with engineers and data scientists to understand the opportunities to help them succeed; they will be your customers!
- Leading as an owner: Owning your work end-to-end and being accountable for the outcomes in the projects you lead, influencing the ML team, partner teams, and landing impact for the business
- Collaborating in and across teams: Build relationships, advocate and communicate with cross-functional partners and product verticals to identify opportunities and bring your technical vision to life
- Being passionate about the work you are doing and contributing positively to Strava’s inclusive and collaborative team culture and values
- We follow a flexible hybrid model that translates to more than half your time on-site in our San Francisco office— three days per week
