1 to 25 of 43 vLLM Jobs in London

open inference. Knowledge of observability stacks, open Bonus Points Experience with AI and machine learning frameworks at scale (e.g., PyTorch, DeepSpeed, Megatron-LM, vLLM). Hands-on experience with major cloud platforms (e.g., AWS, GCP, Azure) and hybrid cloud architectures. Advanced experience with containerization (Docker) and orchestration (Kubernetes), including custom ...

TensorFlow at a working, hands-on level. Distributed training: Horovod, DeepSpeed, Megatron-LM, or equivalent multi-node training frameworks. Inference & serving: NVIDIA Triton, vLLM, TensorRT-LLM, or equivalent high-throughput serving platforms. MLOps/LLMOps: Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex ...

TensorFlow at a working, hands-on level. Distributed training: Horovod, DeepSpeed, Megatron-LM, or equivalent multi-node training frameworks. Inference & serving: NVIDIA Triton, vLLM, TensorRT-LLM, or equivalent high-throughput serving platforms. MLOps/LLMOps: Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex ...

these. They are a bonus, not a checklist. Experience with Delta Lake, Iceberg, Spark or Databricks, PalantirExperience optimising LLM serving with tools such as vLLM, TGI or TensorRT LLMSearch and ranking experience. For example Elasticsearch or rerankersBackground in time series forecasting, causal inference, recommender systems or optimisationExperience managing cloud costs ...

plus Model fine-tuning practical experience with LoRA/QLoRA, supervised fine-tuning, or RLHF workflows is a plus Inference optimization experience with vLLM, TensorRT-LLM, Triton, or model quantization techniques is desirable Observability tooling practical experience with LLM monitoring, tracing, and evaluation frameworks is a strong plus Familiarity with ...

. LLM observability and evaluation tools such as LangSmith, Langfuse, Arize or Weights & Biases. Experience hosting or serving open‐weight models (for example vLLM, Hugging Face, SageMaker). Awareness of AI governance and regulation, including the EU AI Act, ISO/IEC 42001 and GDPR. ITIL, Kubernetes (CKA/CKAD ...

React, Angular), APIs (REST/GraphQL) Machine Learning: Foundations of MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQL LLM tooling: Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management Performance & observability: Skilled in debugging distributed ...

React, Angular), APIs (REST/GraphQL) Machine Learning: Foundations of MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQL LLM tooling: Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management Performance & observability: Skilled in debugging distributed ...

understanding of how models are trained, how they work internally, and their inherent limitations. LLM Serving stack: Experience with the LLM serving stack (e.g. vLLM) for open weight models for bringing the latest models to internal users. ML Literacy: Experience evaluating probabilistic ML systems and managing model "tool use", contexts ...

reranking Experience evaluating RAG systems properly (e.g. recall@k, faithfulness) Streaming platforms such as Kafka Agentic or multi-hop retrieval, GraphRAG, query rewriting vLLM or similar for on-prem model serving LangChain, LlamaIndex, RAGAS or Langfuse Government, defence or national security programmes Desirable Experience Working Environment Central London, SC-cleared ...

Proven ability to solve complex problems and work independently in fast-moving environments. Nice to haves: Experience with modern LLM serving frameworks such as vLLM, SGLang, TensorRT-LLM, or text-generation-inference. PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This ...

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 ...

FedRAMP Experience building developer tools, IDEs, code editors (e.g. Monaco, VS Code extensions) or code analysis tooling Experience serving custom or fine-tuned models (vLLM, TensorRT-LLM, Triton or similar) and GPU infrastructure Rust or C/C++ experience Experience selling to and deploying in regulated or security-conscious enterprises ...

Architectures: Hands-on experience building with OpenAI and Anthropic models/APIs; strong knowledge of LLMs and agent architectures. Tooling: Practical experience with LiteLLM, vLLM, Langfuse (or equivalent LLM observability tooling). Experience with Claude Code/Codex and associated coding-agent workflows is a plus. Evals: Proven ability ...

venues such asNeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, orHPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experienceapplyingpost-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation ...

deployment pipelines and scheduling across clusters and providers. What You Bring Strong Python and system-architecture skills. Experience deploying models with PyTorch, Hugging Face, vLLM, SGLang, TensorRT-LLM, or similar. Experience with queues, scheduling, traffic control, and fleet management at scale. Experience with Linux, Docker, and Kubernetes, and with orchestration ...

APIs, SDKs, CLIs, notebooks Infrastructure: storage, networking, IAM, observability, infrastructure-as-code Familiarity with tools such as PyTorch, Ray, Slurm, MLflow, Langchain, Hugging Face, vLLM, Triton, or similar. Able to balance performance, cost, usability, flexibility, and reliability. Strong prioritisation, communication, and stakeholder management skills. Comfortable taking ambiguous problems from discovery ...

with the open‐weight model landscape and the trade‐offs between regional and multilingual models Experience with an inference serving engine behind the gateway — vLLM, SGLang, or TGI for production serving; Ollama for lightweight on‐prem — and sizing GPU capacity for self‐hosted models (KV‐cache footprint, concurrent batch size ...

engineering & synthetic data pipelines Agentic frameworks, reasoning, tool use, memory systems Evaluation & benchmarking (LLM-as-judge, safety/alignment metrics) Inference optimisation (quantization, distillation, vLLM/TensorRT-LLM) Requirements: - PhD or Master's in CS/AI/ML/Physics/Maths or equivalent research experience - Strong hands ...

design, operational risk and engineering trade-offs. Fluent Python and daily use of coding agents. NICE TO HAVE Inference engines such as SGLang or vLLM, and RL training frameworks such as veRL, Miles or TRL. Agent environment and evaluation frameworks such as Harbor. Sandboxed execution for agents, such as Firecracker ...

training frameworks such as veRL, Miles or TRL, RL environment frameworks such as Harbor or Prime Intellect's verifiers, and inference engines such as vLLM or SGLang. Building agent harnesses and long-horizon tasks with tool use, such as search, code execution and external APIs. Designing learning signals for long ...

engineering experience, ideally with hands‐on work in inference optimization, model training, GPU programming, or ML infrastructure You’re familiar with the serving (e.g., vLLM, SGLang) and training (e.g., slime, verl, TRL) toolchains and can go deep on at least one (you don’t need all of them ...

throughput-optimized setups. Driving consistent “playbook” style deployments for common models and use cases. Become a power user of different runtimes such as vLLM, SGLang, and TRT-LLM and all the common configurations and tradeoffs between them Drive POC and project execution , including: Scoping POCs and keeping stakeholders aligned ...

other image-intelligence work.* Text recognition in low-quality images and video, including non-Latin scripts.* Serving open-weight models efficiently in production, with vLLM or similar.* Distributed training across multiple GPUs.## What you'll getWe're a small, performance-driven team at one of the fast-growing startups ...

post-training Experience with distributed training and GPU systems Strong experimental judgement Ability to work comfortably across ML research and systems engineering Optional vLLM, SGLang, Ray, FSDP or Megatron-LM CUDA, Triton, CuTE or GPU performance optimisation Shortlisted candidates will be contacted within 48 hours. #J-18808-Ljbffr ...