76 to 85 of 85 vLLM Jobs in the UK

Opportunity We are seeking a Senior Software Engineer with a passion for open-source AI infrastructure to work on deploying, extending and optimising vLLM (and potentially other inference serving engines) to support our projects. You will play a crucial, high-impact role across both our key programmes, the Scaling Inference … autonomous agents for software development. What You'll Do Deploy, instrument and monitor open weight models served using vLLM. Implement new features within vLLM to support novel hardware architectures as part of the Scaling Inference Lab. Work with the Panopticon team to identify opportunities to extend vLLM to enhance accuracy ...

generation Depth in multi‐agent systems, planning, program synthesis, or retrieval over structured artefacts such as codebases Deep familiarity with the internals of SGLang, vLLM, or comparable inference serving frameworks - scheduler design, memory management, and execution pipelines A background in another field that studies systems of interacting heterogeneous components - neuroscience ...

results focus. What you will do: Operate the LLM estate: run LoRA fine-tuning cycles and evaluation gates on our GPU hardware, manage vLLM serving (including multi-adapter deployments) alongside production, promote or roll back model versions on the gate results, and keep the serving watchdogs healthy. The CEO retains … world system of record. Python for scripting, data processing or pipeline work. Preferable: Deeper LLM experience: fine-tuning methodology, corpus design, evaluation-harness construction, vLLM internals, or heavy daily use of AI coding agents. ERP, FSM or works-management domain experience job lifecycles, scheduling, SLAs, parts, timesheets, invoicing or heat ...

placement, quota management, and utilization systems across heterogeneous accelerator environments.Optimize inference pipelines for latency, throughput, reliability, memory efficiency, and cost using frameworks such as vLLM, Triton Inference Server, TensorRT, or equivalent serving stacks.Partner with ML engineers and researchers to remove bottlenecks in training, evaluation, batch inference, online inference, deployment … monitoring, and cost optimization.Strong systems engineering fundamentals across Linux, networking, storage, containers, Kubernetes, distributed runtimes, and production debugging.Experience with ML serving frameworks such as vLLM, Triton Inference Server, TensorRT, TorchServe, KServe, Ray Serve, or equivalent systems.Proficiency in Python for infrastructure automation, tooling, debugging, integration, and operational workflows.Practical understanding of performance ...

CommonAI CIC is seeking a Senior Software Engineer to help deploy, extend and optimise vLLM and related inference engines for our AI infrastructure projects. You will work across the Scaling Inference Lab and the High Assurance programme to drive performance, reliability and safety in production‐grade systems. You will collaborate ...

backed challenger lab building state-of-the-art computer-use agents. The inference team owns the full stack from engine layer (vLLM, SGLang) through to serving architecture (disaggregated inference, intelligent routing). The team operates at the intersection of research and production - translating cutting-edge techniques directly into the systems … least one systems language - Rust, C++, or Go Hands-on experience with PyTorch or JAX in an industry setting Experience with inference frameworks: vLLM, SGLang, TensorRT-LLM Solid distributed systems fundamentals and experience operating production ML infrastructure Working knowledge of modern ML including transformers and multimodal architectures Optional Bonus Research ...

Job ID: 200037336Posted: 2026-09-07Location: United Kingdom, Cambridgeshire, CambridgeEmployment type: Full-TimeWork site: 3 days/week in-officeRole type: Individual ContributorTravel: Less than 25%Profession: Research, Applied, & Data SciencesDiscipline: Research SciencesCompany: MicrosoftOverviewThe ...

depth profiling of large-scale inference pipelines, specifically focusing on KV cache management and heterogeneous memory scheduling. AI Serving: Optimising high-throughput frameworks (vLLM, Ray Serve, PyTorch Distributed) to ensure low-latency, multi-tenant performance. Research Leadership: Contributing to top-tier venues (OSDI, NSDI, EuroSys, MLSys) and driving those innovations … Stack: Strong proficiency in C/C++ for systems work, with Python for rapid prototyping. Expertise: Hands-on experience with LLM serving frameworks ( vLLM, Ray Serve, TensorRT-LLM ) and distributed algorithms. Mindset: A solid grounding in systems research methodology and performance profiling tools. The "Value Add" (Desired): A PhD focused ...

implementation level. Attention variants, KV cache strategies, quantisation schemes, and how they shape kernel design You've worked with production inference or training frameworks, vLLM, Megatron-LM, etc You've built performance-critical infrastructure before - compilers, profilers, auto-tuners, or search systems You have real intuition for evolutionary methods, fitness … work of François Chollet, Kenneth Stanley, Jeff Clune, Jurgen Schmidhuber, David Ha, and Christian Szegedy Bonus: Open-source kernel contributions (FlashAttention, FlashInfer, vLLM, Unsloth, Liger-Kernels, ThunderKittens) Publications in ML/AI, kernel optimisation or evolutionary methods (NeurIPS, ICLR, CVPR, GECCO or equivalent) Other HW experience (AMD, MLX, edge ...

direction you shape across the platform. Responsibilities Own the Runtime & Serving Stack: Design, build, and extend the distributed inference and serving stack (e.g. vLLM, SGLang, NVIDIA Dynamo, TensorRT-LLM) onto DX-1, rather than treating any layer as a black box. Scale Distributed Inference: Define how inference scales across many … runtime/network/accelerator boundary. Demonstrated ownership of a hard, end-to-end systems problem, ideally extending a distributed inference/serving stack (vLLM, SGLang, NVIDIA Dynamo, TensorRT-LLM) in production, with specifics on what you built or changed and why. Distributed inference at scale: parallelism strategies, collective communication ...