51 to 58 of 58 vLLM Jobs in England

Improve LLM and VLM endpoint latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, and NVIDIA Dynamo. Develop production model-compression workflows covering quantization, quantization-aware training, distillation, low-bit serving, and accuracy … Hands‐on experience deploying or optimizing LLM, VLM, or high‐throughput transformer inference. Practical knowledge of at least one modern inference stack, such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or an equivalent internal system. A strong understanding of transformer inference bottlenecks involving ...

between self-hosted and external models according to the needs of each workload. You will set the technical direction for inference engines such as vLLM and SGLang, and serving platforms such as KServe. You will build foundations that product, delivery and customer teams can rely on. You will own both. … capability should grow and, over time, shape the team around it. Where you need to be strongest You have operated inference engines such as vLLM or SGLang, or serving platforms such as KServe, under production traffic, latency and reliability constraints. You can explain the boundary between serving orchestration and inference ...

during production rollouts. ImproveLLM and VLMendpointlatency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server,and NVIDIA Dynamo. Develop production model-compression workflows coveringquantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. … PyTorch. Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformerinference. Practical knowledge of at least one modern inferencestack,such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, oran equivalent internal system. A strongunderstanding of transformer inferencebottlenecks involvingKV cache, attention, memory bandwidth, batching ...

endpoints for latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend inference engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, or similar systems. Build and productionize model-compression workflows, including quantization, quantization-aware training, distillation, low-bit serving … experience deploying or optimizing LLM, VLM, or high-throughput transformer inference systems. Practical knowledge of at least one modern inference stack such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or equivalent internal systems. Strong understanding of transformer inference bottlenecks, including KV cache, attention, memory ...

Natural Language Processing (NLP) Researcher We tackle the most complex problems in quantitative finance, bringing scientific clarity to financial complexity from our London HQ. The role involves working with very large text corpora, applying the ...

stack. Investigate efficient execution of workloads across multiple NPUs, including compute/communication overlap and minimizing host-side overhead. Work with AI frameworks like vLLM/SGLang, inference systems and accelerator software stacks to prototype and evaluate new runtime mechanisms. Collaborate with researchers and engineers working across frameworks, compilers, runtime … programming environments such as CUDA, ROCm, OpenCL or CANN/Ascend. Experience with AI frameworks or inference/serving systems such as PyTorch, vLLM, SGLang, ONNX Runtime, or similar systems. Experience with profiling and performance-analysis tools for CPUs, GPUs or NPUs. Knowledge of multi-device or distributed execution, collective ...

production integration. Demonstrate clear latency or throughput gains without unacceptable quality loss. Collaborate with the Inference Foundation team to integrate validated techniques into the vLLM-based serving stack. Partner with pretraining and RL teams when techniques require co-training, retraining, or training auxiliary models to improve inference. Build the right … results, and are eager to take matters into your own hands. Inference or serving experience is a plus, not a requirement. Prior work with vLLM, production LLM deployments, or serving systems will help you ramp up faster. Location and Remote This role is based in our Paris or London offices ...

distributed RL post-training systems, orchestrating trainer, rollout, environment, and reward workloads across thousands of GPUs. Build high-throughput rollout generation, integrating inference engines (vLLM, SGLang), weight synchronization, and asynchronous/off-policy schemes. Design RL environments for agentic, multi-step tasks — sandboxed code execution, tool use, computer use, multimodal … including sandboxed execution and multi-turn tool use. Deep familiarity with RL post-training frameworks (veRL, OpenRLHF, TRL, Ray orchestration) and rollout inference engines (vLLM, SGLang). Strong understanding of GPU clusters, networking, and communication libraries (NCCL, MPI) under mixed training and inference workloads. Nice to Have Running RL training ...