NLP Performance Engineer

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

G-Research is investing in how we apply large language models (LLMs) and other Natural Language Processing (NLP) techniques across the firm. We are looking for an exceptional NLP Performance Engineer to join our NLP Engineering team and take ownership of large-scale LLM inference performance.

This is a specialist Quantitative Developer role. Like all our Quantitative Developers, you will work alongside researchers to bring their ideas to life, with a particular focus on maximising the performance of LLMs.

This is a hands-on, high-impact role. You will design and implement techniques that improve the performance, cost-efficiency and capabilities of inference workloads on cutting-edge compute infrastructure, enabling researchers and engineers to make the best use of current and future systems.

Working closely with research teams and infrastructure engineers, you will profile and analyse workloads, eliminate bottlenecks and develop reference solutions. Your work will help shape the tooling and infrastructure that underpins our NLP capabilities.

Key responsibilities of the role include:

Profiling, benchmarking and optimising large-scale LLM inference workloads across our compute infrastructure

Ensuring efficient deployment of the latest models across a range of GPU architectures, adapting the inference stack as hardware evolves

Designing and implementing inference optimisations while maintaining output quality

Developing reference implementations, libraries and tooling to improve the efficiency and reliability of NLP workloads

Collaborating with researchers, senior stakeholders and engineers to design optimised solutions

Working with systems, architecture and platform teams to evolve the compute stack and influence long-term platform decisions

Who are we looking for?

We're looking for an engineer who combines deep knowledge of LLM inference with strong software engineering skills and a scientific approach to performance.

The ideal candidate will have the following skills and experience:

A Bachelor’s, Master’s or PhD in computer science, or equivalent experience

Proven experience profiling, benchmarking and optimising large-scale LLM inference workloads

A scientific, evidence-led approach to performance optimisation, using rigorous benchmarking and reproducible measurement

Deep understanding of transformer inference, including prefill versus decode, KV-cache behaviour, attention variants and performance bottlenecks

Hands-on experience with LLM serving frameworks such as vLLM, SGLang, TensorRT-LLM or TGI, and the PyTorch ecosystem

Experience with inference optimisation techniques, including quantisation, speculative decoding and model parallelism across modern GPU architectures

Strong software engineering skills, including Python, CUDA and building reliable systems for machine learning workloads

Strong communication skills, with the ability to collaborate across research, infrastructure and engineering teams

Why join us?

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