Machine Learning Performance Engineer

G-Research; Greater London; Hybrid; GBP 90,000 - 150,000; Full time

Benefits offered by this job

Competitive pay

Lunch provided

Annual leave 35d

Pension 9%

Informal dress code

Healthcare & life assurance

Cycle-to-work

Monthly events

Job summary

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

The role requires strong Python, CUDA, and PyTorch knowledge, plus experience with HPC schedulers and Kubernetes.

Qualifications

• Proven track record profiling, benchmarking and optimising distributed workloads.
• Experience with Python.
• Knowledge of CUDA.
• Experience with HPC schedulers and Kubernetes-based workload orchestration.
• Strong understanding of PyTorch and deep learning frameworks.
• Strong background in data structures, algorithms and parallel programming.
• Deep understanding of Linux OS fundamentals.
• Familiarity with profiling/monitoring tools (nsys, ncu, eBPF).
• Strong communication skills across teams.

Responsibilities

• Collaborate with researchers and engineers to understand compute challenges and design optimised solutions.
• Profile, benchmark and tune large-scale training and inference workloads on distributed 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.

Skills

Profiling workloads

Benchmarking

Optimising distributed workloads

Python

CUDA

HPC schedulers

Kubernetes

PyTorch

Data structures & algorithms

Parallel programming

Linux fundamentals

Profiling tools (nsys, ncu, eBPF)

Communication

Education

Bachelors/Masters/PhD in CS

Tools

Kubernetes

Job description

We tackle the most complex problems in quantitative finance, by bringing scientific clarity to financial complexity.

From our London HQ, we unite world-class researchers and engineers in an environment that values deep exploration and methodical execution - because the best ideas take time to evolve.

Together we’re building a world-class platform to amplify our teams’ most powerful ideas.

As part of our engineering team, you’ll shape the platforms and tools that drive high-impact research - designing systems that scale, accelerate discovery and support innovation across the firm.

Take the next step in your career.

The role

We are seeking an exceptional ML Performance Engineer to optimise large-scale workloads across our GPU and CPU infrastructure.

This is a hands-on, impactful role. You will design and implement techniques that improve performance and capabilities of research workloads on cutting-edge compute infrastructure, ensuring our researchers and engineers can make the best use of current and future systems.

You will work directly with internal research teams and infrastructure engineers to profile and analyse workloads, eliminate bottlenecks and develop reference solutions.

Your work will influence long-term platform evolution and help shape the architecture, software stack and tooling that underpins large-scale machine learning computation.

Key responsibilities of the role include:

• Collaborating with researchers, senior stakeholders and engineers to understand their compute challenges and design optimised solutions.
• Profiling, benchmarking and tuning large-scale training and inference workloads for performance on distributed CPU, GPU and memory-intensive jobs.
• Developing reference implementations, libraries and tools to improve job efficiency and reliability.
• Collaborating closely with systems, architecture and platform teams to evolve our compute stack.
• Influencing long-term platform and infrastructure decisions.

Who are we looking for?

The ideal candidate will have the following:

• Bachelors, Masters or PhD degree in computer science, or equivalent experience.
• Proven track record of profiling, benchmarking and optimising distributed workloads.
• Experience with Python.
• Knowledge of CUDA.
• Experience with HPC schedulers and Kubernetes-based workload orchestration.
• Strong understanding of one or more deep learning frameworks, such as PyTorch.
• Strong background in data structures, algorithms, and parallel programming on heterogeneous systems.
• Deep understanding of Linux OS fundamentals, such as as scheduling, memory management, NUMA, networking, and filesystems.
• Familiarity with profiling and monitoring tools, such as nsys, ncu, eBPF-based tools, and performance counters.
• Strong communication skills with the ability to collaborate across research, infrastructure and engineering teams.

Why should you apply?

• Highly competitive compensation plus annual discretionary bonus
• Lunch provided (via Just Eat for Business) and dedicated barista bar
• 35 days’ annual leave
• 9% company pension contributions
• Informal dress code and excellent work/life balance
• Comprehensive healthcare and life assurance
• Cycle-to-work scheme
• Monthly company events

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