Machine Learning Engineer

About the role

ABOUT ELICIT

Elicit https://elicit.com is building the reasoning layer for science and decision-making. We use language models to search over 125 million papers, extract data, and surface insights so that researchers, policy-makers, and industry leaders can go from questions to evidence-backed decisions in minutes.

Today, hundreds of thousands of researchers have used Elicit to speed up literature reviews, automate systematic reviews, and explore new domains. As we expand our impact beyond academic research, we are laying the groundwork for ML systems that are systematic, transparent, and unbounded https://blog.elicit.com/ai-safety/ when reasoning at scale.

To do this, Elicit is pioneering supervision of process, not outcomes https://ought.org/updates/2022-04-06-process. Instead of favoring large black-box models, we break complex questions down into human-legible steps and supervise the reasoning process itself. This approach delivers more transparent, defensible answers today and charts a safer path toward advanced AI tomorrow.

Our vision is ambitious: we’re building the default starting point for understanding and reasoning through any hard question. We invite you to help us build that future.

(See how people use Elicit today on Twitter https://twitter.com/elicitorg; explore our vision in the roadmap https://ought.org/updates/2022-04-08-elicit-plan.)

ABOUT THE ROLE

As a Machine Learning Engineer at Elicit, you’ll build products and workflows that help researchers and scientific teams make higher quality decisions with language models.

This is not a role for someone who only wants to develop models in isolation from user impact. A large part of the work is software engineering: building product experiences, APIs, data integrations, evaluation systems, and reliable harnesses that make language models reliably useful and trustworthy in high-stakes domains.

You’ll work on problems like:

- Turning messy, ambiguous research tasks into clear product experiences

- Building interfaces and artifacts that help users understand, trust, and act on model outputs, thinking beyond the chat interface while leveraging full model capabilities

- Combining language models with external tools, structured and unstructured data, and retrieval systems

- Improving quality through building careful evaluations, truth-conducive model environments and tools, and targeted ML modeling where the impact is high

WHAT YOU’LL BUILD

- Agentic harnesses for target assessment, evidence synthesis, and experiment planning that allow models to provide guarantees about their processes

- Data integrations across literature, scientific databases, customer data, and internal tools

- APIs that customers can use in their own systems

- Evaluation systems that help us understand whether a change actually improves user outcomes

- Trust and transparency features, like source-quality signals, intermediate reasoning, and better ways to inspect and fix outputs