Performance Engineering Manager

Overview

In this Engineering Manager role, you will lead the Compute Performance Engineering team in London, guiding efforts to optimise large-scale workloads across Linux HPC and Kubernetes environments. You’ll shape the long-term strategy to maximise compute utilisation and directly influence the platform that underpins cutting-edge ML research. You’ll mentor engineers, communicate impact to diverse audiences, and partner with researchers to deliver efficient, scalable solutions. This role combines hands-on technical leadership with architecture and tooling decisions that accelerate discovery.

Pay / Benefits

• competitive compensation + discretionary bonus
• Lunch provided (Just Eat for Business)
• barista bar
• 35 days’ annual leave
• 9% company pension contributions
• healthcare and life assurance

Responsibilities

• Define and deliver the long-term performance engineering strategy across G-Research to maximise compute utilisation
• Provide leadership, mentoring and career development for engineers
• Communicate performance improvements and team impact to technical and non-technical audiences
• Engage with researchers, senior stakeholders and engineers to understand compute challenges and design optimized solutions
• Profile, benchmark and tune large-scale workloads across CPU, GPU and memory-intensive jobs
• Develop reference implementations, libraries and tools to improve job efficiency and reliability
• Collaborate with systems, architecture and platform teams to evolve the compute stack
• Influence long-term platform and infrastructure decisions

Key requirements

• Experience managing and developing engineers
• Strong background in computer science (BSc, MSc, PhD or equivalent)
• Proven expertise in profiling, benchmarking and optimising distributed or large-scale workloads
• Proficiency in one or more programming languages with strong grounding in algorithms and performance optimisation
• Deep understanding of Linux internals (scheduling, memory management, NUMA, networking, filesystems)
• Experience with HPC schedulers and Kubernetes workload orchestration
• Hands-on experience with heterogeneous compute (GPUs, multi-core CPUs, high-memory systems)
• Excellent communication and collaboration skills across research, infrastructure and engineering domains
• Familiarity with profiling and monitoring tools (perf, eBPF, VTune, Flamegraphs, Prometheus, Grafana)

• leadership
• mentoring
• clear communication
• profiling and benchmarking
• distributed/large-scale workload optimisation
• Linux internals

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