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
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!
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.
Fellowship Frequently Asked Questions
How long is the program?
The program is 4 months on a full-time basis. We do not currently offer a part-time option.
How much does it cost?
The program is free to the fellows.
Where are you located?
We are located in San Francisco and London.
Can the fellowship program be done remotely?
Key aspect of the learning is the in-person communication with mentors and other fellows. We don't believe the same level of collaboration is possible remotely so we currently do not offer this option.
Do you sponsor visas?
Currently we do not have the ability to sponsor visas.
Bay Area is expensive, do you offer any stipend or living accommodations?
At this point we don't offer any assistance.
Why this Fellowship?
How is this program different from other Data Science programs?
The fellows work on actual machine learning products that are used in production environments. Fellows work under the supervision of the mentor team. Mentors are actively involved in the delivery of projects, including coding.
Fellows also have an opportunity to interact directly with our customers and get immediate feedback on their results.
What happens to fellows after they graduate? What jobs do they get?
Our fellows are now in machine learning roles at Uber ATC, Facebook, Enlitic, Sentient Technologies, Yelp, Orange, Pivotal, etc.
What type of projects will I get a chance to work on?
We apply deep learning and large-scale optimization expertise to finance and adversarial problems. Most of our projects involve deep learning and reinforcement learning on large data sets.
What does the day-to-day look like?
Majority of the time is spent pair programming. We pair up a fellow more proficient in quantitative skills with a fellow more proficient in software development. The project team typically consists of two fellows working under supervision of a mentor.
We have daily scrums, and we are very diligent about it. We have internal slack channels, shared github repos and trello boards. We have a weekly retrospective and iteration planning.
What tools will I get a chance to learn?
We are primarily a python shop but fellows are free to use whatever tool and technique they believe is best suited to the problem. We typically use a variety of machine learning libraries including TensorFlow, Keras, XGBoost, etc.
What percentage of the fellowship is actual model building?
Model building is an iterative process. Typically, we spend 50% on data wrangling, 40% on modeling, and the remaining time on explaining results to business people.