Jason Bohne

I’m a Senior Machine Learning Engineer at Bloomberg, where I work on agents for data annotation and autoresearch pipelines.

I earned my Ph.D. in Applied Mathematics and Statistics from Stony Brook University in 2026, advised by Pawel Polak. My research developed algorithms for online bilevel optimization and preference alignment, with applications to reinforcement learning, LLM alignment, and order execution. This work has appeared at venues including NeurIPS, AISTATS, and ICAIF.

I first came to Bloomberg as a research intern on the ML Strategy team in the CTO Office, where I applied my research to downstream financial tasks, distributing codebases, demos, and tutorials to engineering teams and clients. Before grad school I studied Math at the University of Illinois at Chicago, where I co-founded QTC, the school’s first quant finance group. I got my start in industry as one of the first interns at Alpaca, where I organized community conferences on algorithmic trading and market data.

Outside of work, I write about ML and AI on my blog and build a few side projects.

I’ll be at NeurIPS 2026 in Atlanta — reach out if you’d like to meet up.