At a Glance
- Tasks: Build AI-powered drug discovery tools and enhance scientific workflows.
- Company: Perceptic, a cutting-edge AI company transforming pharma.
- Benefits: Competitive salary, flexible hours, and opportunities for rapid career growth.
- Other info: Fast-paced environment with high ownership and direct feedback.
- Why this job: Join a mission-driven team making a real impact on human health.
- Qualifications: 3+ years in full-stack engineering with AI product experience.
The predicted salary is between 70000 - 90000 £ per year.
The highest calling for AI is improving human health. At Perceptic, we’re building the AI operating system that powers drug discovery - a unified intelligence layer that connects evidence, reasoning, and action across the entire drug lifecycle. We were built by operators from Palantir who spent years deploying production AI in the world’s most demanding enterprise environments. We know what it takes to make ambitious technology hold up under real pressure. Now we’re applying that to pharma and we’re hiring engineers who’ve operated at the same standard.
We are hiring Senior Full-Stack Engineers who have shipped real AI product experiences. We mean people who have built AI products, broken them in production, fixed them, and shipped them to users who depend on the result. Curiosity about AI isn’t enough. The hard problem here is getting AI to reason over the world’s scientific evidence well enough that a pharma scientist will stake a major decision on the result. That takes agents doing substantive work, surfaced through interfaces that make their reasoning easy to follow and check.
Our stack is React and TypeScript on the front end, Java and Python services behind it, and an agent layer built on durable, fault-tolerant workflows that reason over scientific data through a growing set of tools and integrations. We keep the technology choices few and high-leverage, and you’ll work across all of them.
What you’ll own:
- End-to-end product features across the frontend, backend, data, and API layers, owned through to production AI-powered scientific workflows: agentic systems, retrieval, tool use, and the evaluation loops that keep them honest.
- The integration work that turns model providers and AI tooling into product experiences people can depend on.
- Evaluation and feedback systems that make AI behavior measurable and improvable over time.
- Trust surfaces such as provenance, citations, and confidence, rendered so a scientist can check the work behind any answer.
- Close work with founders, customers, and domain experts, turning ambiguity into shipped product.
- Engineering velocity through automation, AI-assisted development, and internal tooling that lets a small team do the work of a much larger one.
What we’re looking for:
- At least 3 years of professional engineering experience, with broad full-stack range.
- You’ve shipped AI features that people rely on, and you can point to them.
- You can talk concretely about prompts, evals, failure modes, latency, cost, and the UX tradeoffs of building with language models.
- You use AI coding tools fluently and have strong, earned opinions about where they help and where they fall down.
- You’ve come from somewhere that held a high bar, such as FAANG, a top AI lab, or a respected product-engineering environment.
- You want more ownership, speed, and upside than those environments tend to offer.
- You have strong product taste and can make pragmatic calls without waiting for perfect requirements.
This is a high-intensity, high-stakes role. We move quickly and give direct feedback, the responsibility you carry will likely outrun your title, and you’ll go wherever the company needs you most, which sometimes means unglamorous work that matters more than anything else that week. The people who do well here find that kind of ownership and growth exciting, and they weren’t looking for a nine-to-five.
