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
Own Elicit's scholarly search and data platform: its coverage, freshness, quality, reliability, and cost as the product and its sources change. Turn what customers and the business need into a technical plan for the area, working closely with product, sales, and partners. Bring new kinds of evidence into Elicit and make them useful to LLM-based features, keeping structure, citations, and provenance intact. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 5+ years of software engineering, including production systems you owned and architecture decisions you made yourself
- Strong Python. You'll use it every day, and our interviews include live coding. Working knowledge of TypeScript too, React is a plus
- Experience designing, debugging, and tuning distributed data pipelines. Spark is especially useful, and comparable distributed-systems experience counts
- Solid SQL, plus a practical feel for partitioning, joins, columnar formats, and how data layout affects performance and cost
- Experience integrating messy external data: APIs, bulk exports, file drops, changing schemas, rate limits, partial failures, and overlapping records
- Care for data quality, entity resolution, provenance, and observability, and a plan for when things fail
More about this role
Elicit is an AI research agent that helps scientists and decision-makers find, synthesise, and understand research. Pharma and biotech teams rely on it for systematic reviews and other work where the evidence has to hold up. We're a public benefit corporation, founded to radically improve reasoning for high-stakes decisions, and our engineering team is small and works in an AI-native way.
Elicit is only as useful as the evidence underneath it. That takes far more than a large corpus of academic papers. We bring together scholarly literature, clinical trials, specialised scientific databases, licensed publisher content, and our customers' private data, then make all of it usable in cited, agentic research workflows.
You'll own a big part of that. You'll improve the distributed systems that build and update our search corpus, make adding a new source repeatable, and turn publisher and enterprise requirements into secure, dependable product features. This is product engineering with a strong data-systems core. It isn't an analytics or BI role.
It's also a full-stack role. A new source only matters once researchers can see and use it, so part of your time goes to the product on top:...
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