Strava is looking for a Senior Machine Learning Platform Engineer to develop and expand their machine learning platform, enabling the company to build models of higher quality with less friction, and ensuring 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
- Have built backend production 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
- Have worked on complex, ambiguous platform challenges and broken them down into manageable tasks with both strategies and tactical execution.
Responsibilities
- Own scalable platform: Lead key projects to level up Strava’s ML tools and system in a way that grows with our use cases, model architectures, and athletes.
- Build for a well-loved consumer product: Work at the intersection of AI and fitness to enable product experiences used by tens of millions of active people worldwide.
- Shape AI at Strava: Be a strong voice and mentor on a highly collaborative team with a range of experience levels. Work across teams to deploy ML solutions across multiple surfaces and expand our technical ML capabilities.
- Build from a rich dataset: Help us make use of Strava’s extensive unique fitness and geo datasets from millions of users to extract actionable insights, inform product decisions, and optimize existing features
- Driving innovation with product in mind: Stay up-to-date with the latest research in machine learning, AI, and related fields. Experiment, advocate, and gain buy-in for innovative techniques to enhance our existing platform, resulting in step-function changes to how we build AI at Strava.
- Raising the ML standard: Mentor engineers to shape how we do ML at Strava. Raise the standards for model development, deployment, and maintenance, and be a go-to source of knowledge of the field.
- 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.
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.
- Being passionate about the work you are doing and contributing positively to Strava’s inclusive and collaborative team culture and values
- Demonstrated strong interpersonal and communication skills, and a collaborative approach to drive business impact across teams.
- We follow a flexible hybrid model that generally translates to half your time on-site in our San Francisco office— three days per week.
