Software Engineer (Scheduling)

As a Software Engineer (Scheduling), you will work on the question at the centre of any large-scale compute platform - what runs, where and when? You will build the scheduling logic and systems that match demanding scientific and machine learning workloads to the resources available, so researchers get answers sooner and every unit of compute does real work., * Design, build and run scheduling systems that allocate compute across a wide range of research and production workloads.

• Balance competing priorities such as fairness, throughput, latency and cost, and turn those trade-offs into clear, testable policies.
• Work with machine learning researchers and scientists to understand how their workloads behave, then shape the platform around their needs.
• Build observability and tooling so you can see how resources are used, find bottlenecks and show the impact of your changes.
• Raise the reliability and scalability of the distributed systems that underpin the company’s research and production work.
• Write clean, well-tested, maintainable code, and contribute to design reviews and engineering standards across the team., * A mission that matters. Your work supports an effort to make drug discovery faster and more precise, with the potential to improve patients’ lives.
• Serious backing. A funding round above $2 billion means the freedom to tackle ambitious problems and the resources to do them properly.
• Real ownership. Scheduling sits at the heart of the platform, so the decisions you make will be felt across the organisation.
• Exceptional colleagues. You will work alongside engineers, machine learning researchers and scientists, and learn from all three.
• A London home base. Join a growing team in one of Europe’s strongest technology and life sciences hubs.

• Strong software engineering fundamentals and a track record of shipping and operating production systems.
• Experience with distributed systems, resource management or workload scheduling, or a clear drive to go deep in these areas.
• A practical approach to hard problems: you reason about trade-offs, measure results and iterate.
• Clear communication and a collaborative style, with the confidence to work closely with researchers and engineers from different backgrounds.
• The ability to work from the London office., If you’d like to know more about the team, the problems and what a day in the role looks like, drop me a line. I’m happy to have an honest, confidential chat, and there is no pressure whether you are actively looking or just curious.

Traditional drug discovery is slow, expensive and often unsuccessful. A London-based AI drug design company is trying to change that by treating biology as a problem that can be engineered, computed and predicted. It has secured billions in funding to scale its AI drug design engine, and it is turning that capital into strong engineering capacity.

The ambition is enormous, and so is the machinery and expertise behind it. Models at this scale need huge amounts of compute, and that compute needs to be orchestrated brilliantly. Engineers here work shoulder to shoulder with scientists and machine learning researchers, and the software they build directly shapes how fast the science moves.