Senior Machine Learning Engineer

San Francisco /
Engineering /
Full Time
At Tempo, you'll be responsible for improving our pipeline for processing 3D data of human exercise and for deepening our engines for workout recommendations, difficulty scaling, and personalization. We're looking for a creative problem solver who has broad knowledge of a variety of fields within machine learning, and is interested in building real world solutions that impact our customers directly.

Tempo aims to deliver the first comprehensive fitness training that's based on real science, not bro science.

Basic Qualifications

    • M.S. in Computer Science or related technical field
    • Fluency with one or more modern neural network frameworks such as Tensorflow, Torch, or Theano
    • 5+ years programming experience in C++
    • 3+ years of work experience in Machine Learning or Artificial Intelligence
    • Strong mathematical background (3D geometry, linear algebra, numerical methods, algorithms)

Preferred Qualifications

    • 5+ years experience with Python
    • Familiarity with the state of the art in supervised learning research
    • Experience with cloud computing environments (AWS/Azure/GCE)
    • Immediate availability
About Tempo
Tempo is a next-generation home fitness system – and the first and only weight training solution that can track your motion and use that data to give you a richer, more effective, and safer workout in live and on-demand classes. Using 3D sensors and A.I., Tempo enables expert coaches to correct your form and provide targeted feedback in real-time. Our flagship product is a combination hardware, software, and content-streaming device that packages an immersive 42” touchscreen display, competition-grade weight set, and other accessories in a sleek, free-standing industrial design.

Headquartered in San Francisco, Tempo's all-star team includes alumni from Google, YouTube, Netflix, Airbnb, Pixar, and Orangetheory, backed by $17.5M in Series A funding from Founders Fund, Khosla Ventures, DCM, and Signal Fire.