ML Performance Engineer – Scale GPU/CPU Workloads

Barlowe LLP; Greater London; On-site; GBP 90,000 - 150,000; Full time

Benefits offered by this job

Lunch provided

35 days’ annual leave

9% company pension contributions

Healthcare

Life assurance

Cycle-to-work scheme

Monthly company events

Job summary

G-Research is seeking an exceptional ML Performance Engineer to optimise large-scale workloads across GPU and CPU infrastructure. You will profile and tune training/inference workloads, develop reference implementations and collaborate with research and platform teams to evolve the compute stack.

The role shapes platform evolution and enables researchers to push the boundaries of machine learning. You will work with Python, CUDA, Kubernetes, and deep learning frameworks like PyTorch, applying

Qualifications

• Degree in computer science or equivalent experience.
• Proven track record profiling, benchmarking and optimising distributed workloads.
• Experience with Python and CUDA for ML workloads.
• Exposure to HPC schedulers and Kubernetes-based orchestration.
• Strong knowledge of PyTorch and DL frameworks.
• Solid understanding of data structures, algorithms and parallel programming.
• Deep Linux OS fundamentals and networking basics.
• Familiar with profiling/monitoring tools (nsys, ncu, eBPF counters).
• Excellent communication across research, infrastructure and engineering teams.

Responsibilities

• Collaborate with researchers and engineers to understand compute challenges and design optimised solutions.
• Profile, benchmark and tune large-scale training/inference workloads on CPU, GPU, and memory-heavy jobs.
• Develop reference implementations, libraries and tools to improve efficiency and reliability.
• Work with systems/architectures to evolve the compute stack and platform decisions.

Skills

Profiling

Distributed workloads

Python

CUDA

Kubernetes

PyTorch

Linux

eBPF

NSYS/NCU

Parallelism

Communication

Education

Bachelor/Master/PhD in Computer Science or equivalent

Tools

nsys

ncux

eBPF

Job description

G-Research is seeking an exceptional ML Performance Engineer to optimise large-scale workloads across GPU and CPU infrastructure. You will profile and tune training/inference workloads, develop reference implementations and collaborate with research and platform teams to evolve the compute stack.

The role shapes platform evolution and enables researchers to push the boundaries of machine learning. You will work with Python, CUDA, Kubernetes, and deep learning frameworks like PyTorch, applying