Platform Engineer

Join a global, technology-driven organisation as a Platform Engineer, designing and building distributed systems that power large-scale computational research infrastructure.

The role sits at the intersection of distributed systems, platform engineering, and high-performance computing (HPC) , working on foundational services used by highly technical research and engineering teams.

A global technology-led organisation operating large-scale, data-intensive and computationally demanding systems. Engineering is central to the business, with significant investment in distributed computing, research infrastructure, and high-performance platforms.

You will join a specialist platform engineering team responsible for building the infrastructure and services underpinning large-scale computational research.

1. Design, build, and operate distributed services managing compute workloads and data access across large-scale infrastructure
2. Develop platform APIs and tooling to improve researcher and developer productivity
3. Improve the reliability, scalability, and performance of compute-intensive workflows
4. Build solutions across workload orchestration, resource management, observability, and security
5. Translate complex research and infrastructure requirements into scalable platform capabilities
6. Take end-to-end ownership from system design and implementation through to production operation

1. 8-12+ years building and operating large-scale distributed systems in production
2. Strong programming skills in C++, Java, Rust, Go , or similar systems languages
3. Strong understanding of distributed systems, concurrency, performance, reliability, and fault tolerance
4. Experience designing platforms, services, or infrastructure used by other engineers
5. Degree in Computer Science or a related technical discipline, or equivalent practical experience

1. Internal developer platforms, cloud infrastructure, orchestration systems, or HPC
2. GPU-accelerated computing and/or ML infrastructure
3. PyTorch, TensorFlow, or similar frameworks
4. Experience supporting researchers, data scientists, or highly technical engineering users
5. Large-scale, data-intensive or performance-sensitive environments

1. Solve complex distributed systems and infrastructure problems at significant scale
2. Build platforms that directly accelerate computational research
3. Work on challenging problems across HPC, distributed computing, orchestration, and platform engineering
4. High degree of technical ownership and autonomy