AnswerThis is building the research workspace for scientific knowledge inside enterprises. Our agents search, synthesize, and draft evidence-based research autonomously. More than 200,000 researchers use the platform across universities and Fortune 500 companies. We’re YC-backed, growing fast, and small on purpose.
What we’re solving
Trillions of dollars flow into global R&D every year. Researchers still spend enormous amounts of time manually finding papers, writing literature reviews, and assembling evidence packages. This work decides whether new drugs and medical devices reach patients. We’re turning it into a living, compounding system.
Who we’re looking for
A senior engineer who has built AI agent systems in production and led teams at startups. You have shipped under uncertainty, without perfect specs, on timelines that did not make sense on paper.
You will own the technical architecture and product velocity end to end, from customer problem to deployed solution. You will work directly with enterprise customers, make hard scope calls with limited information, and measure success by business impact.
You will build the engineering team over time. You know what works at three engineers breaks at ten, and you want to design that transition.
What you’ll own
• Architecture and execution across Next.js, Python/FastAPI, retrieval, agents, and production infrastructure
• Fast product iteration with researchers and enterprise customers
• Reliability, quality, and observability for high-stakes scientific workflows
• The engineering culture and team we build next
Interview process
• 20-minute culture screen
• One-hour technical deep dive
• Take-home
• Two-week paid trial
Compensation
$170,000–$240,000+ base with 0.2%–0.5% equity.
This role is in person in San Francisco. If you have only operated inside large engineering organizations with established processes, or optimize for elegance over business impact, this is not the right fit.
Skills Required
- Experience building AI agent systems in production
- Experience leading teams at startups
- Ability to ship products under uncertainty and with limited specifications
- Experience owning technical architecture and product execution end to end
- Experience with Next.js
- Experience with Python and FastAPI
- Experience with retrieval systems and production infrastructure
- Ability to work directly with enterprise customers and make scope decisions
- Ability to build and scale an engineering team
What We Do
AnswerThis builds an AI-powered research workspace for students, academics, scientists, and research teams. Its platform searches a large corpus of scholarly papers, identifies relevant evidence and research gaps, and helps users analyze sources, summarize findings, and produce citation-backed literature reviews and drafts. It also provides tools to organize papers and research libraries, collaborate with others, and support scientific discovery across academic and professional workflows.
.png)







