Machine Learning Fellowship

training the next generation of data scientists
by building real-world machine learning applications

San Francisco | New York


Data Science is not knowledge to be acquired but rather an art that must be learned through practice.  The number one qualification employers look for when hiring a data science candidate is previous experience.  The Startup.ML fellowship gives aspiring data scientists the chance to hone their skills by building real-world machine learning applications.  

  • build scalable machine learning models with agile software development methodology
  • full-time for 4 months
  • mentoring by seasoned data scientists
  • pair program with other fellows and mentors
  • we are based in San Francisco and Berkeley
  • apply latest research in deep learning, ensemble learners, optimization techniques, etc.

Fellows have an opportunity to work on Startup.ML's growing portfolio of finance products in areas like quantitative trading and portfolio construction as well as startup projects from a variety of industries.

Fellows from previous cohorts are now in data science roles at Uber Advanced Technologies Center, Enlitic, Sentient Technologies, Yelp, Orange,  etc.


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
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.
— Alex Chao, Uber ATC



Featured Hiring Partners

I’m really impressed by everything the Startup.ML team is doing - after spending time on-site, we decided to hire one of the fellows for our medical deep learning company.
— Jeremy Howard, Enlitic

Our Work



One prominent area of our focus since inception has been researching of systematic trading strategies using artificial intelligence.  Karén Chaltikian leads this area of our research. His background includes more than 17 years in financial industry, including a decade at a hedge fund in BlackRock.  

Multibillion dollar hedge funds and prop traders currently trade with strategies that were developed in our fellowship program.



Adversarial.AI is one of the first products launched from the fellowship program. Within adversarial domains our key focus areas include security, fraud and monitoring.  

We provide a cloud-based AI solution for adversarial machine learning that is used by some of the world’s largest telecommunications, transportation and financial services companies.



We also partner with startup incubators and accelerators to offer a mentorship program for startups interested in using machine learning to create differentiation.  We have active partnerships with Batchery and Acceleprise .

Fellowship to Maximize Practical Experience

Fellowship Application Process

Step 1: We offer open enrollment.  Fist complete this form

Step 2: Work on a challenge problem and submit your results

Step 3: Schedule a time to speak with a mentor or ex-fellow

Program Location
Name *
When can you start? *
When can you start?
We have a rolling admission policy. Since there is no set curriculum, we admit fellows based on the needs of our projects.
Familiar with ML theory & math
Know how to code
Proficient in stats
Understand distributed systems

Fellowship Frequently Asked Questions


How long and where is the program?

The program is 4 months long and based in the San Francisco Bay Area.

Can the fellowship program be done remotely? Also do you sponsor visas?

No, you need to be present in person as we believe one learns data science my interacting and exchanging ideas in person. Moreover we mentor the fellows and therefore for effective mentoring the fellows need to be present in the office. No we don't have the capabilities to apply for visas for fellows. 

How much does it cost?

The program is free to the fellows.

Bay Area is expensive, do you offer any stipend or living accommodations?

At this point we don't offer any assistance.

How is this program different from other Data Science programs?

The fellows work on actual industry sponsored projects. These projects span multiple months and the fellows have an opportunity to interact directly with the sponsors. The goal is to deliver a product and not a project. There is also daily interaction with mentors and on Fridays we get industry visitors who share their Data Science experience with the fellows. 

What happens to fellows after they graduate? What jobs do they get?

Our fellows are now in data science roles at Uber Advanced Technologies Center, Enlitic, Sentient Technologies, Yelp, Orange, etc.

What type of projects will I get a chance to work on?

We work on quantitative finance and startup projects.  Our work with hedge funds, prop traders and asset managers represents the majority of focus.  This work is very demanding and requires us to do cutting edge research.  We spend approximately 20% of our time working on startups in a variety of industries to help the community and give fellows a more diverse set of experiences.

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 2 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 munging, 40% on modeling and the remaining on making the project user friendly and ensuring that the model is usable.