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ML Research Resident

Role overview

Qualifications

  • Experience with LLMs
  • Intuition about systematic and verifiable reasoning
  • Concern for AI transparency
  • Strong software engineering background

Responsibilities

  • Develop computational procedures (operators) to improve knowledge states
  • Design and test improvement operators for stability over iterations
  • Start with simple cases and demonstrate reliable iteration
  • Address core challenges in AI transparency and scalable reasoning

Key facts

About the company

Elicit logo

Elicit

Artificial Intelligence & Machine Learning Services

Elicit, the AI research assistant, helps you automate time-consuming research tasks like summarizing papers, extracting data, and synthesizing your findings. We're a public benefit company with a mission is to scale up good reasoning. We want machine learning to help as much with thinking and reflection as it does with tasks that have clear short-term outcomes.

Company details

Company typeStartup
IndustryArtificial Intelligence & Machine Learning Services
Company size11 - 50

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Job description

Elicit is building a research agent that can use an unlimited amount of test-time compute while keeping its reasoning transparent and verifiable.

The residency

Transformers do a fixed amount of computation per token, and the quality of work degrades rapidly when they are applied iteratively. 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.

What is a knowledge state? A knowledge state consists of structured information - for example, a scientific paper might be represented as a set of claims supported by evidence and connected through logical reasoning; this might be combined with scratchpads, evergreen “notes to self”, search trees, and other information.

What counts as improvement? 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. 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.

As research resident, your work will focus on designing and testing improvement operators that maintain stability over 1000+ iterations while making genuine progress. You'll start with simple cases (e.g., shallow refactoring of scientific papers) and demonstrate reliable iteration before scaling to more complex reasoning tasks.

Developing systems that perform legible reasoning over long horizons addresses core challenges in AI transparency and scalable reasoning.

About you

Strong candidates will have experience with LLMs, good intuitions about what makes reasoning systematic and verifiable, and care about AI transparency.

The best applicants will additionally have a strong software engineering background and concrete examples of how they've applied this background to come up with novel abstractions that push the frontiers of automated reasoning.

Logistics

  • 3-month contract role

  • Compensation: $12-15k/month depending on experience

  • Location: In-person (Oakland) or remote (US)

  • Potential of full-time offer for exceptional candidates

Location and travel

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.

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MR

Marcus Rivera

Chief Revenue Officer

m.rivera@company.com
linkedin.com/in/marcusrivera
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