Senior Machine Learning Platform Engineer

About This Role

Strava is the app for active people. With over 150 million athletes in more than 185 countries, Strava is where connection, motivation, and personal bests thrive. No matter your activity, gear, or goals, we help you find your crew, crush your milestones, and keep moving forward. Start your journey with Strava today.

Our mission is simple: to motivate people to live their best active lives. We believe in the power of movement to connect and drive people forward.

We are looking for a Senior Machine Learning Platform Engineer to join the growing AI and Machine Learning team at Strava. This team is responsible for sophisticated machine learning models and systems which provide value to Strava athletes including personalization, recommendations, search, and trust and safety. The team also maintains the ML platform and infrastructure that enables our team to iterate on models quickly and deploy them reliably at scale.

This is an important role on the team to develop and expand our machine learning platform. This lets us build models of higher quality with less friction. It helps ensure our models are served with stability and reliability, while ensuring we monitor model performance carefully. Ultimately you won’t just help with the things we are doing now, but also unlock our technological capabilities for the future.

We follow a flexible hybrid model that generally translates to half your time on-site in our San Francisco office— three days per week.

What You’ll Do:

• 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

You Will Be Successful Here By:

• 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. Ensure the end-to-end system delivers as expected through collaboration with partners.
• 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.
• Being passionate about the work you are doing and contributing positively to Strava’s inclusive and collaborative team culture and values

What You’ll Bring to the Team:

• 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
• Have built backend production services on cloud environments like (but not limited to) AWS, using languages Python, Terraform, and other simila