The quantitative researchers, in addition to having advanced machine learning experience, should have a strong coding background - experience with large code bases and best practices in software development. Many physicists, computational biologists, neuroscientists, etc come from this background, having worked in collaborations and implementing machine learning methods on messy real-world data collected from large experimental setups. We’ve had Fellows like this, who also in their spare time built and trained networks in TensorFlow and then built their own customized layer in C++ behind the scenes.

The software engineers coming into the program would have machine learning experience, but have not necessarily in a full-time role yet. Just like the software engineers that come into our data engineering program, it’s a chicken or the egg problem: employers want to see experience in the role before hiring for it, but how can you get experience if no one wants to take a chance on hiring you. Insight takes that chance, you work on cutting edge ML problems here, then the company has evidence (obviously combined with your previous years of work experience) to than make a bet to bring you on as an ML engineer.

Overall AI practitioners in these roles usually fall along a spectrum, having their main strength be either software engineering or quantitative research. Often companies will have experts on both ends work together to implement current research and then put those models into production.