Databricks AI Research forms team for AI agent evaluation

This is a fantastic team. Check it out if you want to help build agents and models that reliably answer the most challenging analytical questions in biotech, finance, etc for our customers.

I'm building a new team at @databricks AI Research and we're hiring.

We're focused on one of the hardest open problems in AI right now: how do you measure and continuously improve agents that operate on enterprise data at scale. We're looking for founding engineers to build the flywheel that turns evaluation results directly into better agents — from development and training all the way to production.

If you want to work on problems that actually matter at the frontier of AI research, I'd love to talk.

Link in comments 👇

An incredibly exciting opportunity. Come work with us! Lots of fun and impactful problems to work on.

Apply here: https://www.databricks.com/company/careers/engineering---pipeline/staff-software-engineer---agent-quality-8532681002

@julianeagu @databricks This problem is where a lot of agent demos fall apart. Static evals look fine, then the data shifts or permissions get weird. I keep thinking enterprise agent quality is mostly a continuous eval problem.

I’m working on an AI, not an LLM. Doesn’t use a transformer, doesn’t need retraining, and the evaluation problem you’re describing doesn’t really exist in it because every output already shows you exactly which knowledge atoms fired. Different architecture entirely. Happy to share more if you’re curious.

@julianeagu Remote friendly or in person?

@craig_certo @databricks yes!

@julianeagu @databricks I'm building StellarFS it's an experiment about compiled semantic layer https://github.com/BlueCurveCorp/StellarFS/tree/dropzone

@julianeagu @databricks Hi Julia, Just a day ago, in similar space, I did a research work and published my finding in this article. I will definitely apply at the career link, but I am also open to conversation here in case you find below work interesting.

@julianeagu @jefrankle @databricks How important is agentic memory management to you? We built a new memory system we’re presenting at ACM CAIS would love to discuss memory optimization, interpretability and on-GPU databases https://arxiv.org/abs/2605.06997

@julianeagu @databricks its easy you just look at the data.

jokes, but its a good problem to tackle.

@julianeagu @databricks Oh I use genie almost daily for my SLM fine-tuning, and I work rigorously on optimizing multi-agentic workflows as well. Particularly my interest is in memory architecture of ai assistants and stateful agentic workflows.

PS: I'm not a 6 year exp tho. 1-3 yr exp is where I'll be

@julianeagu @databricks Hi Julia, I'm interested in the team and would love to learn more! Just sent a dm :)

@julianeagu @databricks MIT engineer here. Interested!

@Tralallama in person!

@julianeagu @databricks Hi Julia,

I’m Saurabh Singh, a second-year student and an aspiring AI/full-stack developer. I’m still a fresher, but I’m deeply interested in AI systems, agents, and scalable engineering.

I’d love to connect and learn more about the team

@julianeagu @databricks Applied to this link, never received a response.

@julianeagu @databricks Would you sponsor a visa or support remote role?