> Markdown version of [/jobs/ext/1435018-nlp-performance-engineer](https://www.wearedevelopers.com/jobs/ext/1435018-nlp-performance-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # NLP Performance Engineer - **Company:** G-Research - **Location:** London, UK - **Contract:** Permanent contract - **Skills:** Profiling, Nvidia CUDA, Python (Programming Language), Machine Learning, Performance Tuning, Software Engineering, Pytorch, Large Language Models, Information Technology, TensorRT - **Published:** July 25, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=1c30e3fa69aca9a0 ## About the Role 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 ## Description 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 ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [Profiling Symfony & PHP apps with Blackfire](https://www.wearedevelopers.com/videos/265-profiling-symfony-php-apps-with-blackfire) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) - [Enhancing Workload Security in Kubernetes](https://www.wearedevelopers.com/videos/356-enhancing-workload-security-in-kubernetes) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [The Best Large Language Models on The Market](https://www.wearedevelopers.com/magazine/319-the-best-large-language-models-on-the-market) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)