Job Description

Our LINQ platform is the software that plans, schedules, and drives life-science labs. Our clients run labs for cancer diagnostics, drug discovery, cell manufacturing, synthetic biology and more.

This role owns the interface to the lab — not just the UI, but every surface someone builds on or acts through: the app a scientist touches, the APIs and SDK an integrator builds on, the CLI an engineer scripts with, the agent surface a model acts through. Most product interfaces are forms over a database. Ours front a physical world that changes by itself — robots act, schedules change, instruments fail — and every one of those surfaces has to keep its user genuinely in control.

We're building the next generation of our interfaces — AI-native from the ground up: personalised to role and behaviour, UI generated on the fly where it earns its place, and agents working in the product alongside the people using it. This is a role for someone user-obsessed — the platform underneath is enormously capable and enormously complex, and the product wins by how much of that complexity users never have to see. And the stakes only grow: the more autonomous the lab becomes, the more the interface matters, because it's where human judgment enters the system.

The problems you'd be working on:

Explaining automation to a scientist. When the plan changes underneath the user, the interface has to show what moved and why. When a workflow can't run, 500 is not an answer a human can act on — turning our engine's output into something a scientist can fix is an unsolved design problem. And the timeline it lives on is long: processes that run for months, hundreds of plates in flight.

The worst moment is the main event. Users arrive precisely when something has gone wrong: a plate misplaced, a task failed, retry-or-failover decisions with live samples degrading while they think. The interface has to put the right context in front of them — not a wall of state — at the exact moment the stakes are highest.

Authoring is programming, whether we admit it or not. Scientists design workflows on a canvas today, and it's static: you express an intent, submit it, and find out later whether it actually runs. It should feel like an IDE — validation as you type, the constraints visible while you're authoring, simulate the run you're designing and watch it execute before a single robot moves. Design and execution are disconnected today, and closing that gap is one of the biggest wins available to us.

Interfaces that build themselves. An operator, a scientist, and a lab manager should not see the same screen. Surfaces personalised to role and behaviour, generated where it helps — and the open question you'd own: where does generated UI aid discoverability, and where does it erode trust?

Four front doors, one lab. A scientist in the browser, an integrator on the API and SDK, an engineer scripting the CLI, an agent acting over MCP — the same capabilities have to show up on every surface, each one idiomatic to its user. An API that is hard to use correctly is wrong, and DX is UX: the developer and the agent are users too.

The role:

• Own end-to-end squad delivery within Product Engineering (Software), in a department with Product, Engineering, Design, Data & AI (PEDDA)—turning challenges into predictable, high-quality product delivery
• Hands-on squad leader–set the bar of what high-quality looks like at pace
• Own part of the technical architecture–set the direction of our product
• Work as one unit with Product and Design — you help decide what we build, not just build it
• Be the squad's force multiplier — the patterns, components, and tooling you build make everyone ship better and faster
• Lead, and level-up high-performing Product Engineers that report to you—hiring, mentoring, and setting an uncompromising bar for execution
• Architect, and continuously evolve operating systems that scale—eliminating bottlenecks, increasing velocity, and enabling the squad to move autonomously
• Shape the future of how we deliver — our AI-native, automation-first way of building is being defined right now, and you'd set the pattern for the team rather than inherit one
• Bridge strategy and execution—ensuring we build what we defined; that what we build, ships fast; and what we ship, delivers results with high quality

The stack:

We use the right tool for the job.

• React (TS) single paged app
• Public GraphQL APIs, MCP for the agent surface, gRPC for driver surface, SDKs, CLIS
• Kubernetes hosted on our own hardware clusters
• Live lab state streamed from NATS JetStream into the browser
• Go and Python services behind the contracts
• Large fleet of robots and hundreds of lab instruments on the other side of every scree

You:

• You've built genuinely complex, dynamic user interfaces — realtime data, live state, visualisation at scale — and can talk about their failure modes for an hour.
• You're fullstack by instinct: the UI a scientist touches, the API an integrator builds on, the BFF behind them, down to the events streaming off the machines — and you judge every layer by what it does for the user.
• You're user-obsessed: you watch people use what you built, feel their friction personally, and measure success by their outcomes — not by features shipped.
• You run at complex domains: you want to understand how a lab actually works — the science, the robots, the constraints — because you can't distill what you don't understand.
• You've taken complex features from idea to shipped, end to end — not incremental improvements to someone else's design — and you can point at the product impact.
• You don't wait for a PM to hand you a spec: you help shape the problem, resolve ambiguity rather than escalate it, push back when something doesn't feel right, and care about the speed and polish of what ships.
• Strong JavaScript/TypeScript; Go a bonus. You've integrated AI into products.
• You've led senior engineers before and know the difference between leading and managing — you do the former from inside the code.
• 10+ years engineering, 6+ building products, in environments where both speed and quality were non-negotiable.

Logistics:

London, 3 days a week in office (near Angel).

Why this, why now:

• The problems are hard in a way user-experience work rarely gets to be: live state from a physical world, high-stakes decision surfaces, and users who are scientists, developers, and agents — with real consequences.
• The outcomes are real: our platform runs in labs working on cancer diagnostics, drug discovery, and synthetic biology. Faster labs mean faster science.
• The team is small and senior, ownership is high, and both the next generation of the interface and the AI-native way we'll build it are being designed now — you'd shape them, not inherit them.

UK Team Benefits:

🍎 Vitality Health Insurance

👀 Eye Care

🚗 Salary Sacrifice - EV

🚲 Salary Sacrifice - Bike & Tech

🧘🏼‍♀️ Wellbeing & Support

☀️ Wellbeing & Development Allowance

💙 Spill & Employee Assistance Programme

😊 Additional Leave

👵🏼 Pension Scheme

🫂 Group Life & Critical Illness cover

We are an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Discrimination of any kind based on race, colour, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status is strictly prohibited.