Machine Learning Engineer

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 - Buildi