Elicit helps you synthesize evidence, understand the landscape, design programs and experiments, draft proposals, and weigh tradeoffs. Trusted by over 7 million researchers and 8 of the top 20 pharmaceutical companies. Backed by Basis Set Ventures.
About the role
You'll join our engineering team and report to an Engineering Manager. You'll work on a cross-functional engineering team, collaborating closely with other engineers, product, design, and domain experts, taking problems from investigation and experimental design through implementation and use in the product. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Good experimental instincts. You can devise a useful test when there isn't an established benchmark, distinguish evidence from noise, and recognize when your evaluation is rewarding the wrong behavior. You're willing to inspect individual failures in detail
- Practical familiarity with language models. You've explored their capabilities and limitations enough to have informed ideas about how to improve them. You can choose an approach based on the problem and update it as models change
- Effective use of coding assistants. You use AI tools to increase what you can build while taking responsibility for understanding the design and verifying the result
- We care about evidence of these abilities. Substantial projects, thoughtful experiments, and clear explanations of technical decisions are useful ways to show us how you work
More about this role
Elicit helps research and business teams investigate hard questions and make high-stakes decisions grounded in evidence.
Our ambition is to make high-quality reasoning under uncertainty available at a much greater scale in contexts where stakes are high and feedback is sparse. We're building AI systems whose work people can inspect, question, and improve, so that increasingly capable AI also makes us more wise.
Some of the most valuable questions people face are also the hardest to answer well. Which research program should a team advance? What evidence would change its priorities? Which uncertainty is worth resolving before committing to a multi-year, $100 million dollar trial?
Today's models can contribute to these decisions, but they often miss a crucial consideration, make an assumption they should have checked, or reverse a recommendation too readily when presented with new information. Further, because they can perform increasing amounts of work autonomously, their process and reasoning is often poorly legible, so decision makers are wary to trust their outputs. Helping with these problems requires a deep understanding of model behaviors, what matters for different types of...
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