12 of 12 Remote/Hybrid vLLM Jobs in London

Foundry and other AWS, Azure or GCP services . Work with self-hosted inference and model-serving technologies where appropriate, including tools such as vLLM, SGLang and Ollama . Design evaluation, observability and monitoring frameworks to assess model performance, system health, reliability and business impact. Optimise AI solutions for performance ...

optimise models using cloud services like AWS Bedrock and Azure AI Foundry, or self-host them on GPU/CPU hardware using tools like vLLM, SGLang, and Ollama.Solution Evaluation: Implement frameworks and approaches to evaluate model performance against business objectives, both pre-deployment and on an ongoing basis as part ...

purely technical preference.Technical Expertise:Deep proficiency in Python and modern software development practices.Hands-on experience with Distributed Training infrastructure (Multi-node GPU training, Kubernetes, vLLM).Familiarity with Deep Learning frameworks (PyTorch).Experience with MLOps tools and experiment tracking (e.g., ClearML, MLFlow, Weights & Biases).Research Fluency: Ability to read technical research ...

major cloud Hands‐on experience with LLM infrastructure: a model gateway pattern (such as LiteLLM or in‐house), model serving (such as vLLM or managed endpoints), and vector or retrieval systems Agent‐systems experience: you have built with an agent orchestration framework (such as LangGraph or first‐party agent SDKs ...

Hands-on experience with LLM training cycles, parameter-efficient fine-tuning (PEFT), and sophisticated prompt engineering.Inference Stack: Experience with high-performance inference servers (e.g., vLLM, TGI, or Triton) and an understanding of how to optimize models for GPU deployment.Infrastructure: Comfortable working in Linux-based environments and proficient in managing containerized ...

experience with LLM training cycles, parameter-efficient fine-tuning (PEFT), and sophisticated prompt engineering. Inference Stack: Experience with high-performance inference servers (e.g., vLLM, TGI, or Triton ) and an understanding of how to optimize models for GPU deployment. Infrastructure: Comfortable working in Linux-based environments and proficient in managing containerized ...

production-quality code and data pipelines for ML systemsProficiency in modern AI development frameworks including: PyTorch, Jax , HuggingFace Transformers, LLM APIs (litellm etc) and vLLM for building and deploying large-scale AI applicationsUnderstanding of LLM training methodologies including instruction fine-tuning, preference optimization, and reinforcement learning approachesStrong software engineering skills ...

Experience with training LLMs or other large transformer architectures.Contributions to ML frameworks (PyTorch, JAX, DeepSpeed, Megatron, xFormers, etc.).Familiarity with evaluation and serving frameworks (vLLM, TensorRT-LLM, custom KV caches).Experience with data pipeline optimization, sharded datasets, or caching strategies.Background in performance engineering, profiling, or low-level systems.Bonus: paper ...

without degrading their reasoning capabilities. Experience with machine translation, multilingual NLP, or language quality estimation. Familiarity with inference and serving at scale (e.g. via vLLM, SGLang, TensorRT‐LLM, etc) and long‐context modelling. Publications at top‐tier venues. What we offer Diverse and internationally distributed team : joining our team means ...

compatible object storage - including performance-killing cases (small-object overhead, range-request patterns, eventual consistency, multipart tuning). Comfort with model-serving runtimes (vLLM, SGLang etc) and the formats they consume (safetensors, GGUF, sharded checkpoints). An end-to-end view: comfortable reasoning about NIC, switch, filesystem, cache, container runtime ...

user and a working results. Bonus Points These aren't requirements, but they'd make you stand out: Experience with inference serving stacks (vLLM, SGLang, TensorRT-LLM, Triton). A track record of contributing to or maintaining open source developer tools and ML ecosystem projects Experience writing technical documentation ...

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 ...