Engineering Manager (ML and Data Products)

Join Strava, a well-loved consumer product at the intersection of fitness and geospatial technology. As an Engineering Manager, you will lead a high-impact data and machine learning team, drive the strategy and execution of data products, and foster a collaborative culture. You will have the opportunity to work with extensive unique fitness and geo datasets, treat data products as products, and collaborate across disciplines. Strava offers a range of benefits, including 100% company-paid benefits for employees and families, flexible paid time off, and a generous professional development stipend.

• Contribuer de manière pratique aux solutions que nous livrons dans le produit, en travaillant à l'intersection de la condition physique et de la géospatialité.
• Gérer, encadrer et développer une équipe d'ingénieurs en apprentissage automatique, d'ingénieurs en données et de scientifiques des données pour livrer des expériences alimentées par l'IA et les données.
• Piloter la feuille de route des produits de données, en possédant tout, de la prototypage initial du modèle à son déploiement en production, à son évolutivité et à son optimisation.
• 2 years of experience managing an AI/ML engineering team, with a proven track record of growing engineers and delivering complex technical projects
• Technical experience building, shipping, and supporting complex ML models in production at scale
• Hands on coding for model development and serving in backend service development on cloud environments (AWS preferred), using Python
• Excellent communication and collaboration skills, with the ability to influence and align stakeholders across multiple engineering and product teams
• Demonstrated track record of solving complex, ambiguous machine learning problems and broken them down into strategies and tactical execution for teams
• Interested in production ML model operational excellence and best practices,including scalable ML architecture, serving optimization and dataset versioning
• Experience building and maintaining production data pipelines and batch/stream workflows using technologies like Spark, Kafka, Snowflake, or similar