About AnswerThis

The client is accelerating the path to scientific discovery by automating the most tedious part of research: literature reviews. Over 300,000 scientists across top universities and companies use AnswerThis daily. The product crossed $2.1M ARR, is cash-flow positive, and is used by researchers at Stanford, MIT, Amazon, and top institutions worldwide. Backed by Y Combinator (F25), Phosphor Capital, and leaders at OpenAI and Google.

We build high-volume AI agents that do scientific evidence work — finding papers, analyzing experiments, writing drafts, updating literature reviews — at scale. Our agents run multi-step, long-running jobs in real time, and our next engineering priority is bringing failure rates as close to zero as possible.

About the Role

We're hiring a senior engineer to take over the technical work our founder is currently doing — architecture decisions, code reviews, agent infrastructure, and evals. This is not a feature-building role. You'll own the reliability, scalability, and architecture of a production agentic system used by 300,000+ scientists daily.

You'll work remotely from Latin America, 6 days a week (flexible which day on the weekend), with PST/EST overlap required.

What You'll Work On

• Reduce agent failure and deviation rates across long-running, multi-step workflows
• Design and implement durable execution systems — background jobs, async tasks, retry logic, human-in-the-loop steps
• Build and maintain evals and benchmarks to measure agent performance
• Architect scalable infrastructure to support millions of users
• Own code reviews and technical decision-making alongside the founding team
• Expand product capabilities and ship new features end-to-end

Priority Order for This Role

1. Production agentic AI experience — has built and deployed agentic workflows to real users at scale, with proper fail-safes, not structured generation or prototypes
2. Agent DevOps and infrastructure — durable workflows, long-running jobs, async execution, failure handling, observability
3. Full-stack product experience — has shipped customer-facing features end-to-end

Requiments:

• Production agentic AI experience — built agents used by real users with measurable scale and reliability requirements
• Deep understanding of agent architecture, context window management, and agentic DevOps
• Experience with durable execution frameworks (Temporal, Restate, or similar), background jobs, and async workflows
• Strong full-stack engineering: TypeScript, Next.js, Python
• Comfortable across the full stack — frontend, backend, infrastructure
• Experience shipping customer-facing products end-to-end
• Excellent communication skills — this person will eventually help grow and lead the engineering team
• PST/EST timezone overlap required