Machine Learning Infrastructure Engineer

ABOUT THE ROLE

The AI/ML organization works on ML-powered consumer products that incorporate computer vision and recommender systems into the fitness domain. We are looking for a Software Engineer to drive ML infrastructure and operations for the AI/ML teams. The main focus will be to work closely with ML Engineers, Data Engineers, Software Engineers, and Data Scientists to help support the future of ML development within connected fitness. The Engineer will build the connective tissue between the data infrastructure and ML teams focusing on vital tools and infrastructure to support model development and deployment pipelines, CI/CD, testing and offline experimentation at scale. This is an outstanding opportunity in the industry for someone to work on infrastructure and tooling that supports both computer vision as well as recommender systems.

YOUR DAILY IMPACT AT PELOTON

• Help build, evolve, and scale innovative machine learning system infrastructure powering Peloton’s connected fitness data.
• Work with other ML Engineers, Researchers and Backend Engineers to implement scalable infrastructure solutions for ML model development, model lifecycle management, model monitoring, and offline experimentation.
• Work on building an internal training platform that accelerates the velocity of offline experimentation for ML Engineers.
• Collaborate with other ML Engineers and Data Engineers to build and deploy data stores that support batch pipelines as well as real-time recommendations.

YOU BRING TO PELOTON

• Experience developing infrastructure and platforms to power ML at scale.
• Programming background, with experience in Python, experience with C, C++, Java, or more general purpose programming languages is a plus.
• Experience with multiple technologies from the following list: AWS, MLFlow, Airflow, PySpark, Jupyter, Kubernetes, MySQL & NoSQL databases, Kubeflow.
• Bonus: Experience in setting up ML CI/CD pipelines (Jenkins / GHA), testing and validating code and components, testing and validating data, data schemas, and models.
• Bonus: Working with large datasets with distributed data processing frameworks like Spark.
• Bonus: Building an internal training platform that supports multiple ML engineers with their offline experimentation.

#LI-Hybrid #LI-RF2