Research Engineer

You'll be one of the first members of a small research team, working close to both the data and the product.

- Build evaluation from real data - Mine production workspace data (carefully, with consent and redaction pipelines you'll help design) into benchmarks and labeled datasets. Design label schemas, run labeling with real inter-rater rigor, and build the harnesses that make expert judgment cheap to capture and hard to corrupt.

- Run experiments that ship - Initial work happens on offline workspace data; the destination is production systems used by every Ando customer. The distance between "the benchmark improved" and "the feature shipped" should be weeks and you'll own both ends.

- Decide what we build versus who we partner with - We won't do everything in-house. Part of the job is evaluating frontier vendors and research teams (eval infrastructure, observability, continual-learning tooling) and choosing who we build with.

- Publish when we have something real - We expect the team to publish as we make meaningful progress. But ideas are not Ando's moat; execution, product quality, and customer experience are. Research here is in service of customers first, and the publications will be better for it: they'll describe things that actually worked on real data.

- Strong applied research background , with depth in model evaluation, benchmarking, and/or failure analysis. You've built evals you trusted enough to make decisions with.

- Evidence over credentials. Work samples or code that demonstrate the skills: eval frameworks, benchmark suites, failure-analysis reports or tooling, labeling infrastructure. Show us something you built to find out whether a system actually worked.

- Strong technical communication. You can explain complex ideas simply and hold high-bandwidth, generative technical conversations with researchers and with our product team.

- Comfort with mess. Real workspace data is incomplete, ambiguous, and full of edge cases that break clean abstractions. You treat that as signal, not noise.

- Bonus: familiarity with simulation ( Park et al. ), human-in-the-loop evaluation ( Scale HIL leaderboard ), Cartridges and related context/memory-compression work, memory for multi-party long-running settings, or agent observability standards (setting up Langsmith or similar).