Senior Machine Learning Engineer

About the role

Strava is seeking a Senior Machine Learning Engineer to join our growing AI and Machine Learning team in San Francisco, working in a flexible hybrid model with a expectation of three days per week in our office. In this role, you will own the design, prototyping, and end-to-end delivery of sophisticated machine learning systems that power core athlete experiences such as personalization, recommendations, search, and trust and safety. You will collaborate closely with product managers and cross-functional partners to translate business goals into scalable ML solutions and ensure reliable, high-performance deployment. You will be accountable for driving innovation while maintaining best practices in model development, monitoring, and operational excellence. This position offers the opportunity to work on systems used by tens of millions of active people worldwide and directly influence how movement motivates people to live their best active lives.

Key facts

What you'll do

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.

Drive innovation with Product in mind: Stay up-to-date with the latest research in machine learning, AI, and related fields. Experiment, advocate and get buy-in for innovative techniques to improve existing products or explore new features that result in step function changes to how we build AI at Strava.

Lead as an Owner: Own your work end-to-end and be accountable for the outcomes in the projects you drive and landing impact for the business. Ensure the end-to-end system delivers as expected through collaboration with partners.

Analyze the Data: Work closely with product managers, data scientists, and engineers to find opportunities for applying machine learning to drive business impact and enhance Strava's features and measure impact.

Collaborate 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.

Raise the ML standard: Help work towards best practices for model development, deployment, and maintenance.

Be passionate about the work you are doing and contributing positively to Strava's inclusive and collaborative team culture and values.

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.

Are comfortable working in a fast-paced, ambiguous environment while managing multiple priorities and driving projects to completion.