Data Platform Engineer

As a Data Platform Engineer, you will build the foundations that let researchers and machine learning teams move quickly. Your platform will carry the data behind AI-driven drug design, and the quality of what you build will shape how fast the science moves., * Design, build and run a data platform that supports large-scale machine learning and scientific research

• Create reliable, well-monitored data pipelines that turn complex, varied scientific data into something teams can trust and use
• Work closely with ML researchers, scientists and software engineers to understand what they need, and build the tooling that removes friction from their work
• Set high standards for data quality, reliability, security and observability across the platform
• Improve performance, scalability and cost-efficiency as data volumes and ambitions grow
• Shape the technical direction of the platform, bringing sound engineering judgement to architecture decisions

• Proven experience building and operating data platforms or data infrastructure in production
• Strong software engineering fundamentals, with a habit of writing clean, tested, maintainable code
• Hands-on experience designing and running data pipelines at scale
• A collaborative approach, with the confidence to work alongside researchers and engineers from different disciplines
• Curiosity about how AI can accelerate science, and the drive to solve hard problems in a fast-moving environment

• Join a business with substantial investment and the resources to pursue a genuinely big goal
• Build core infrastructure that sits at the heart of the science
• Work alongside people at the leading edge of AI and the life sciences, and learn from them daily

If this sounds like the kind of problem you want to spend your next few years on, get in touch for a confidential chat. I’m happy to talk through the role, the team and whether it fits where you want to go next.

Drug discovery is slow, expensive and costly. We’re working with a London-based AI drug design company that is working to change that. It is building an engine that designs medicines computationally, so drug developers don’t have to rely on years of trial and error at the bench.

The ambition is matched by the scale in which they are growing and gaining attention. The firm has secured billions in investment to scale its drug design engine, backed by major players in tech investment, and have partnerships already with leading pharmaceutical companies. The accolades put it among the best-resourced AI-for-science businesses globally. Its head office is in London, and the work sits where machine learning, biology and chemistry meet.

Behind every model that proposes a promising molecule is a mountain of data that has to be trustworthy, fast to reach and ready to use, which is where you come in.