Free Fellowship Focused on
San Francisco, New York, London, Singapore
We give 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, New York, London, Singapore; we are accepting applications for future programs in São Paulo, Shanghai and Zürich
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
Network of Past Fellows
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!
Trevor Lindsay, Facebook
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.
Stephanie Oh, Sentient Technologies
Fellowship.AI 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.
Alex Chao, Uber ATC
Enrolling in the program at Fellowship.AI 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
Luis Zertuche, Ten-X
We follow a trimester system that divides the year into three terms of 14-16 weeks each. Here are important deadlines you should be aware of:
May 1 - Aug 31, 2018
Feb 1: Rolling admission
Mar 30: Deadline for applications
Apr 9: Challenge deadline
Apr 20: Interview deadline
Apr 23: Final notifications
Sep 3 - Dec 24, 2018
Jun 1: Rolling admission
Jul 31: Deadline for applications
Aug 13: Challenge deadline
Aug 24: Interview deadline
Aug 27: Final notifications
May 1 - Aug 31, 2019
Feb 1: Rolling admission
Mar 29: Deadline for applications
Apr 8: Challenge deadline
Apr 19: Interview deadline
Apr 22: Final notifications
Jan 7 - Apr 30, 2019
Oct 1: Rolling admission
Nov 30: Deadline for applications
Dec 3: Challenge deadline
Dec 14: Interview deadline
Dec 24: Final notifications
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, New York, London and Singapore. We are accepting applications for future programs in São Paulo, Shanghai and Zürich.
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.
The program is offered in expensive cities, do you offer any stipend or living accommodations?
At this point we don't offer any assistance.
What are my chances of getting in?
Our acceptance rate is currently 6% of all applicants. Your chances are significantly higher if you complete the challenge exercise.
What prior knowledge is required to succeed in the program?
The challenge problems will give you an idea of what is expected from successful candidates. We look for creative problem solving ability, basic coding proficiency (particularly in python) and a foundational understanding of machine learning theory and methods.
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.
We also offer paid externships through our commercial arm, Launchpad.AI.
What type of projects will I get a chance to work on?
We apply deep learning and large-scale optimization expertise to variety of industry 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, PyTorch, 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.