Join an elite team and help the world's biggest companies become AI powerhouses.


Our mission is to make AI the default operating system of business for world’s largest companies. We help create their AI strategies, train their teams, solve their hardest problems with data science, build their machine learning infrastructure, and much more.

Prolego’s principles are an efficient way to share the lessons we have learned along the way with each other and with new team members. They form the foundation of our culture.

What does a typical day look like at Prolego? What kind of work can you expect to do? What do our client engagements look like? Before applying, take a moment and skim our interview guide. It will help you decide whether you are a good fit for what we do.

Data science team

Design novel solutions using math, code, and creativity. Boost your skills while transforming the world's largest companies. We have both manager and individual contributor career paths.

AI Engagement & strategy team

Lead the world's largest companies into the AI future by building strategies and running engagements. Get on an AI leadership career path.

ML Operations Team

Design and build the infrastructure to power AI transformation at the world's largest companies. Become an expert in building MLOps components like data pipelines, model deployments monitoring, and much more.

Our entire engagement team gets data science. You will be working on a world-class team with colleagues who understand and respect what you can do.

Our founder, Kevin Dewalt, built his first neural network in 1994. Megan McGee runs our client engagements and is a data scientist herself.


Every potential employer promises you an opportunity to work on cutting-edge projects, but very few deliver on that promise. Here are examples of projects we have done for some of the world’s largest companies:

Develop a methodology to automate a complex business using inverse reinforcement learning.

Used weak supervision and programmatic approaches to building training data to help one of the largest life insurance companies reduce their legal risks by automating contract review.

Performed a literature search of state-of-the-art approaches to entity extraction for one of the world’s largest security regulators. Ultimately evaluated the LUKE methodology and many other standard techniques.

Used topological data analysis for a more sophisticated visualization of chat conversations at one of the largest life insurance companies.

Developed a bespoke approach for table extraction and transaction reconciliation from PDFs at one of the world’s largest banks.

Developed an MLOps architecture for scaling to thousands of models.

We only work with top organizations and leaders who get it