Elicit

Elicit

ML Research Resident

Oakland, CA (or remote within US timezones) · Contract

Sponsorship not specified$12k-$15kDetected 586 days ago
Machine LearningNLPLLMsResearch

About the role

  • THE RESIDENCY Transformers do a fixed amount of computation per token, and the quality of work degrades rapidly when they are applied iteratively.
  • But unlike typical ML systems that are often trained to do "whatever works", we need improvements that are epistemically sound - each step should make the knowledge state more useful while remaining human-readable.
  • An improvement might reorganize information to better answer a question, find an implicit assumption in an argument, or connect evidence across multiple sources.

Responsibilities

  • Elicit is building a research agent that can use an unlimited amount of test-time compute while keeping its reasoning transparent and verifiable.
  • As research resident, you'll work with us for 3 months on developing computational procedures (operators) that can reliably improve a knowledge state over thousands of iterations.
  • Like scientists, we want LLMs to make genuine progress in understanding - separating inferences from raw evidence, finding connections between ideas, building clearer explanations, and identifying gaps in reasoning.
  • As research resident, your work will focus on designing and testing improvement operators that maintain stability over 1000+ iterations while making genuine progress.
  • Developing systems that perform legible reasoning over long horizons addresses core challenges in AI transparency and scalable reasoning.

Compensation

  • $12-15k/month depending on experience
  • Potential of full-time offer for exceptional candidates
  • We have a great office in Oakland, CA, and we'd love to see you there if you're local.
  • That said, we're just as happy for you to work remotely.
  • We do get the whole team together for a quarterly retreat somewhere fun, because in-person time matters to us.

This listing is sourced directly from Elicit's careers page and normalized into a canonical job model.