Strava-posted 7 days ago
Full-time • Mid Level
Hybrid • San Francisco, CA
251-500 employees

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 Machine Learning Platform Engineer to join the growing AI and Machine Learning team at Strava. This team is responsible for developing sophisticated machine learning models and systems, plus leveraging generative AI technologies. Together this provides value to Strava athletes in various aspects including personalization, recommendations, search, and trust and safety. This is an important role on the team to develop and expand the platform behind the curtain. 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 translates to more than half your time on-site in our San Francisco office— three days per week.

  • 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
  • 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.
  • 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.
  • 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.
  • Being passionate about the work you are doing and contributing positively to Strava’s inclusive and collaborative team culture and values
  • 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
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