{"id":774853,"url":"https://alion.io/job/g-research-nlp-performance-engineer","title":"NLP Performance Engineer","company":{"id":445185,"name":"G-Research","domain":"gresearch.com","url":"https://alion.io/company/g-research","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":75,"open_postings":21,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-10T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":90000,"max_usd":237000,"period":"year","method":"role_country_seniority_unknown","sample_n":131},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"KV Cache","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"SGLang","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"TGI","optional":false},{"name":"vLLM","optional":false},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-07-23T00:00:00Z","employer_posted_date":"2026-07-23","last_verified_at":"2026-10-11T04:49:13Z","board_verified":true,"closed_at":null,"days_open":80,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":80},"description":"We tackle the most complex problems in quantitative finance, by bringing scientific clarity to financial complexity.\nFrom 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.\nAs 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.\nTake the next step in your career.\nThe role\nG-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.\nThis 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.\nThis 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.\nWorking 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.\nKey responsibilities of the role include:\nProfiling, benchmarking and optimising large-scale LLM inference workloads across our compute infrastructure\nEnsuring efficient deployment of the latest models across a range of GPU architectures, adapting the inference stack as hardware evolves\nDesigning and implementing inference optimisations while maintaining output quality\nDeveloping reference implementations, libraries and tooling to improve the efficiency and reliability of NLP workloads\nCollaborating with researchers, senior stakeholders and engineers to design optimised solutions\nWorking with systems, architecture and platform teams to evolve the compute stack and influence long-term platform decisions\nWho are we looking for?\nWe're looking for an engineer who combines deep knowledge of LLM inference with strong software engineering skills and a scientific approach to performance.\nThe ideal candidate will have the following skills and experience:\nA Bachelor’s, Master’s or PhD in computer science, or equivalent experience\nProven experience profiling, benchmarking and optimising large-scale LLM inference workloads\nA scientific, evidence-led approach to performance optimisation, using rigorous benchmarking and reproducible measurement\nDeep understanding of transformer inference, including prefill versus decode, KV-cache behaviour, attention variants and performance bottlenecks\nHands-on experience with LLM serving frameworks such as vLLM, SGLang, TensorRT-LLM or TGI, and the PyTorch ecosystem\nExperience with inference optimisation techniques, including quantisation, speculative decoding and model parallelism across modern GPU architectures\nStrong software engineering skills, including Python, CUDA and building reliable systems for machine learning workloads\nStrong communication skills, with the ability to collaborate across research, infrastructure and engineering teams\nWhy join us?\nHighly competitive compensation plus annual discretionary bonus\nLunch provided (via Just Eat for Business) and dedicated barista bar\n35 days’ annual leave\n9% company pension contributions\nInformal dress code and excellent work/life balance\nComprehensive healthcare and life assurance\nCycle-to-work scheme\nMonthly company events\nG-Research is committed to cultivating and preserving an inclusive work environment. We are an ideas-driven business and we place great value on diversity of experience and opinions.\nWe want to ensure that applicants receive a recruitment experience that enables them to perform at their best. 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