Fellowship Program Focused on
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. Program highlights:
build scalable machine learning models with agile software development methodology
mentoring by experienced ML practitioners
pair program with other fellows and mentors
apply latest research in deep learning, reinforcement learning, generative adversarial networks
full-time for four months
Fellows from previous cohorts are now in data science roles at Uber Advanced Technologies Center, Facebook, Yelp, Google, Salesforce, Orange, and Ernst & Young. 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 Fellowship.AI was one of the best professional decisions I've ever made. It gave me a feel for how real projects with real constraints and challenges unfold beyond an academic data science setting. The immersive nature of the program helped me build perspective on the professional landscape, and build confidence in making the career leap from research to industry."
-Luis Zertuche, Ten-X
“My Fellowship experience exposed me to the industrial applications of deep learning, paving way to kick start my career as a Data Scientist. The externship offered to me in a leading crypto-currency trading company further enhanced my experience in various security aspects, scalability, response times of the models deployed. All of these experiences collectively helped me pursue a full-time career in one of the leading Financial Services company.”
-Sharath Kalkur, Mastercard
“The Fellowship.AI program was the best way for me to transition into a career in data science. I was able to work on multiple commercial projects in a short period of time, as well as connect with others in the ML community. Through the following externship I gained experience abroad delivering large-scale projects to international clientele, and was placed in a role doing cutting-edge work in the industry afterwards.”
-Viv Pitter, Sentient Technologies
Step 1: Complete the application for the cohort / location. You can apply for a maximum of two locations.
Step 2: Join us for an upcoming Ask me Anything session.
Step 3: Work on a challenge problem and submit your solution, if you want to be given priority consideration.
Step 4: Candidates selected to move forward will be invited to schedule a 45-minute interview with a mentor or a former fellow.
We follow a trimester system that divides the year into three terms of 14-16 weeks each. Here are important deadlines to be aware of:
MAY 1 - AUG 31, 2019
Feb 1: Rolling admission
Mar 1: Challenge deadline
Apr 19: Interview deadline
Apr 22: Final notifications
SEP 2 - DEC 20, 2019
Jun 1: Rolling admission
Jul 1: Challenge deadline
Aug 24: Interview deadline
Aug 26: Final notifications
Important: You can apply up to a maximum of two locations. Applications beyond two locations will be automatically rejected. Please pick locations that make sense given your current visa status and place of residence.
Frequently Asked Questions
For questions related specifically to the challenge problems, take a look at the FAQ section on the challenge page.
Program Basics & Application PRocess
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.
Can the fellowship program be done remotely?
A 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.
Can I get in if I don’t submit a challenge?
You will be given priority consideration if you submit a challenge, and your chances of being accepted will significantly improve. We occasionally waive the requirement for exceptional candidates.
The program is offered in expensive cities, do you offer any stipend or living accommodations?
We do not offer any assistance.
What are my chances of getting in?
Our acceptance rate is currently 6% of all applicants. Candidates who perform well on their challenge exercise and make it evident that they have put thoughtful time and effort into it have the highest chance of acceptance.
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.
What do you look for in candidates?
We do not have any specific education or work experience requirements, as we believe that great data scientists and AI practitioners can come from any area of expertise. For this reason, we put much more weight on your demonstrated ability evidenced by your challenge submission and interview performance than we do on your credentials.
We highly encourage applications from candidates in groups underrepresented in AI, whether in terms of gender or gender identity, sexual orientation, ethnicity, age, educational background, or career path.
What can I expect in the interviews?
The first interview, depending on your skills demonstrated in your application and challenge submission, will involve some or all of the following:
Motivation and why you are interested in the fellowship
You challenge solution and the reasoning behind it
Your knowledge of machine learning concepts
Your coding skills (in Python)
Your general data science skills, such as feature engineering
The second call will be a more informal conversation that will give the fellowship leadership the chance to get to know you better, and give you another chance to ask questions.
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, Google, 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?
The 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, and sci-kit learn.
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.