There are a number of interesting technical challenges related to making differential privacy work in production (e.g. implementing novel algorithms for ML and statistical inference, proving privacy properties).
If you are interested in learning more, my company (LeapYear) is hiring differential privacy researchers, as well as software engineers interested in developing an enterprise machine learning platform.
Some background on our team: We recently raised our Series B, and hired VMWare’s first VP of Engineering, who scaled VMWare from 15 to 750+ engineers. Almost all of our backend code is written in Haskell.
On the commercial side, we’ve signed several multi-million dollar contracts with Fortune 100 customers in financial services, healthcare, & tech, and deployed on sensitive data at petabyte scale.
If you are interested in learning more, my company (LeapYear) is hiring differential privacy researchers, as well as software engineers interested in developing an enterprise machine learning platform.
Some background on our team: We recently raised our Series B, and hired VMWare’s first VP of Engineering, who scaled VMWare from 15 to 750+ engineers. Almost all of our backend code is written in Haskell.
On the commercial side, we’ve signed several multi-million dollar contracts with Fortune 100 customers in financial services, healthcare, & tech, and deployed on sensitive data at petabyte scale.
Happy to answer questions and review applications submitted here: https://leapyear.ai/careers