HANDS-ON FELLOWSHIP PROGRAM
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
Apply latest research in deep learning, reinforcement learning, generative adversarial networks
Full-time for four months
Fully remote with optional in-person collaboration
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.
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.
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.
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.
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.