Free Fellowship Focused on Hands-on Experience

Startup.ML fellowship gives aspiring machine learning engineers the chance to hone their skills by building real-world applications.  The number one qualification employers look for when hiring an ML engineering candidate is previous experience.    

  • build scalable machine learning models with agile software development methodology
  • mentoring by experienced ML practitioners
  • full-time for four months
  • pair program with other fellows and mentors
  • apply latest research in deep learning, reinforcement learning, generative adversarial networks, etc.
  • program is offered in San Francisco and London

Fellows from previous cohorts are now in data science roles at Uber Advanced Technologies Center, Facebook, Yelp, Orange,  etc. See a complete list of our past fellows.

 

Hiring Partners & Employers


Former Fellow Testimonials

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Trevor Lindsay, Facebook

Applying to the fellowship was the best thing I could have done for my career. There’s really no other program like it out there where you can take the lead on a project for a hedge fund and deliver a product that will actually be used. I gained invaluable experience in advanced ML methods that boosted my confidence in interviews and landed me where I am today!

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Alex Chao, Uber ATC

Startup.ML provided a community of passionate machine learning practitioners and real world projects that helped solidify and deepen my knowledge, while at the same time instilling confidence in my ability to bring significant, measurable value to clients.

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Stephanie Oh, Sentient Technologies

The program addressed my desire to research the latest deep learning advancements and to interface with and deliver actual products to real clients. Not only did I learn a great deal about machine learning from the mentors, but also how to efficiently manage and deliver a product. 

 

Luis Zertuche, Ten-X

Enrolling in the program at Startup.ML turned out to be one of the best professional decisions I've ever made.  It gave me a feel for how real projects with real constrains and challenges unfold in beyond an academic data science setting. The immersive nature of the program help me build perspective on what the professional landscape looks like, but also to build confidence in making the career leap from research to industry