The Role

Peloton is looking for a Machine Learning/Deep Learning Engineer focused on Computer Vision. You’ll be developing cutting-edge systems to provide our members with a world-class fitness experience, in collaboration with Product, Software, and Hardware teams.


  • 2-5+ years of hands-on, real-world experience with one more of Computer Vision, Machine Learning, Deep Learning. Especially in the domains of Perception, Object Detection, and Segmentation.
  • Proficiency in Python and Java/C/C++, and ML frameworks like PyTorch, Tensorflow, Keras, etc.
  • Ability to translate academic literature into high-quality production code with a strong sense of good system design.
  • Comfortable working with large image and video datasets.

Bonus Points 

Experience with one or more of:

  • Experience deploying to Edge / Mobile, Resource-constrained, and/or Embedded platforms.
  • Model Compression techniques (Quantization, Pruning, Distillation)
  • Caffe/Caffe2 or Core ML
  • Distributed Training
  • Few-shot Learning, Transfer Learning

Please note: This is a full-time position that will be remote initially (due to COVID-19) and based in our New York City HQ once it is safe to re-open the office.

About Peloton:

Founded in 2012, Peloton is a global interactive fitness platform that brings the energy and benefits of studio-style workouts to the convenience and comfort of home. We use technology and design to bring our Members immersive content through the Peloton Bike, the Peloton Tread, and Peloton Digital, which provide comprehensive, socially-connected fitness offerings anytime, anywhere. We believe in taking risks and challenging the status quo by continuously innovating and improving. Our team is made up of passionate brand ambassadors, and we know that together, we go far.

Headquartered in New York City, with offices, warehouses and retail showrooms in the US, UK and Canada, Peloton is changing the way people get fit. Peloton has been named to many prestigious industry lists, including Fast Company's Most Innovative Companies, CNBC's Disruptor 50, Crain's New York Business' Tech25 and Fast50, as well as TIME's Genius Companies. Visit to learn more about joining our team.

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