26 to 45 of 45 Ray Jobs in the UK

Kubernetes, and Terraform Strong scripting skills in Python or Bash Familiar with ML lifecycle tools, model monitoring, and versioning Exposure to tools like KServe, Ray Serve, Triton, or vLLM a big plus Bonus Points: Experience with observability frameworks like Prometheus or OpenTelemetry Knowledge of ML libraries: TensorFlow, PyTorch, HuggingFace Exposure ...

Kubernetes, and Terraform Strong scripting skills in Python or Bash Familiar with ML lifecycle tools, model monitoring, and versioning Exposure to tools like KServe, Ray Serve, Triton, or vLLM a big plus Bonus Points: Experience with observability frameworks like Prometheus or OpenTelemetry Knowledge of ML libraries: TensorFlow, PyTorch, HuggingFace Exposure ...

knowledge of distributed systems, operating systems, machine learning systems architecture, Inference serving, and AI Infrastructure. · Hands-on experience with LLM serving frameworks (e.g., vLLM, Ray Serve, TensorRT-LLM, TGI) and distributed KV cache optimization. · Proficiency in C/C++, with additional experience in Python for research prototyping. · Solid grounding ...

knowledge of distributed systems, operating systems, machine learning systems architecture, Inference serving, and AI Infrastructure. · Hands-on experience with LLM serving frameworks (e.g., vLLM, Ray Serve, TensorRT-LLM, TGI) and distributed KV cache optimization. · Proficiency in C/C++, with additional experience in Python for research prototyping. · Solid grounding ...

knowledge of distributed systems, operating systems, machine learning systems architecture, Inference serving, and AI Infrastructure. · Hands-on experience with LLM serving frameworks (e.g., vLLM, Ray Serve, TensorRT-LLM, TGI) and distributed KV cache optimization. · Proficiency in C/C++, with additional experience in Python for research prototyping. · Solid grounding ...

What We’re Looking For 5+ years leading ML infrastructure or AI platform engineering teams Deep experience in distributed training environments (PyTorch DDP, DeepSpeed, Ray or similar) Strong Python engineering foundations with production discipline Experience building cloud‐native ML platforms (AWS/Azure) Expertise across MLOps, model lifecycle management ...

models. Deep expertise in computer vision methods (detection, tracking, segmentation, multimodal models). Strong command of ML Ops frameworks (SageMaker, Kubeflow, MLFlow, Ray) and modern data pipelines. Background working in sports technology, broadcast, or real-time tracking systems preferred. Track record of leading large-scale technical organizations (50+ engineers/ ...

open-source initiativesYour profile:5+ years building and deploying ML systems at scaleExpertise in PyTorch and modern ML tooling (ML Flow, Weights & Biases, Sagemaker, Ray)Experience in cloud and containerized deploymentInterest or experience in biology, bioinformatics, or healthcareProven track record of building research environments and deploying production-ready modelsBe ...

about twenty machine learning scientists. The team is supported by an ML Ops team that provides state-of-the-art tooling (including AWS, Encord, Ray, PyTorch Lightning and Weights & Biases). The Applied Science team works closely with product engineering to deploy models to serve our worldwide customer base. Position ...

about twenty machine learning scientists. The team is supported by an ML Ops team that provides state-of-the-art tooling (including AWS, Encord, Ray, PyTorch Lightning and Weights & Biases). The Applied Science team works closely with product engineering to deploy models to serve our worldwide customer base. Position ...

about twenty machine learning scientists. The team is supported by an ML Ops team that provides state-of-the-art tooling (including AWS, Encord, Ray, PyTorch Lightning and Weights & Biases). The Applied Science team works closely with product engineering to deploy models to serve our worldwide customer base. Position ...

about twenty machine learning scientists. The team is supported by an ML Ops team that provides state-of-the-art tooling (including AWS, Encord, Ray, PyTorch Lightning and Weights & Biases). The Applied Science team works closely with product engineering to deploy models to serve our worldwide customer base. Position ...

runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using Profiling/Tracing ...

runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using Profiling/Tracing ...

runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using Profiling/Tracing ...

runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using Profiling/Tracing ...

runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using Profiling/Tracing ...

runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using Profiling/Tracing ...

runtime environments; Hands-on experience with Serverless architectures and cloud-native optimization technologies such as containers, Kubernetes, service orchestration, and autoscaling vLLM, SGLang, Ray Serve, etc.); understand common optimization concepts such as continuous batching, KV-Cache reuse, parallelism, and compression/quantization/distillation Proficient in using Profiling/Tracing ...

/or Computer Vision algorithms. Hands‐on experience in implementing distributed/multi‐threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc.). Ability to understand and align with business expectations, and write clear and concise OKRs (Objectives and Key Results). Experience as a "Responsible Owner … ideas at all levels, building trust with stakeholders. Preferred qualifications, capabilities, and skills Experience in designing and implementing pipelines using DAGs (e.g., Kubeflow, DVC, Ray). Ability to construct batch and streaming microservices exposed as gRPC and/or GraphQL endpoints. Demonstrable experience in parameter‐efficient fine‐tuning, model quantization ...