Staff / Principal Research Scientist (US)

You don't need all of this. But you need enough to make a case.

• Foundation models: training, new architectures, RL, reward modeling, scaling

• Evaluation: benchmarks, eval loops, quality measurement, LLM-as-judge, failure analysis

• Frontier topics: multimodal models, agents, tool use, test-time compute, world models

• Published research at ICML, ICLR, NeurIPS, EMNLP, ACL, or AAAI

• PhD in ML/NLP, or equivalent practical experience you can point to

• Public work: non-trivial AI side projects, interdisciplinary experiments, open-source contributions

• Full-stack research ownership: you frame the question, run the experiments, write the paper, ship the result

If you learned through building, competitions, or collaborations outside academia, that counts. We care about evidence, not credentials.

• You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go

• You believe research isn't finished until it’s shipped. You have a bias for impact over purely academic output

• You don't just ship code; you obsess over the why. You’re the first to question an approach if you think there’s a better way to solve the core problem

• You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make

We hand you unclear problems and expect you to make them clear. We value researchers who say "I don't know yet" and then design the experiment that finds out. We treat evaluation as a first-class research product, not a box to check before launch. Impact comes before publications though we support sharing work that moves the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater.

We believe in the power of in-person collaboration to solve the hardest problems and foster a strong team culture. We offer relocation assistance and look forward to you joining us in our Mountain View office.