26 to 50 of 62 Ray Jobs in the UK

strategies, and mentoring senior and mid‐level engineers. Preferred Direct open‐source or production contributions to modern inference frameworks (e.g., vLLM, Triton, TensorRT‐LLM, Ray Serve, or TorchServe). Deep experience with GPU systems engineering and hardware performance optimisation (e.g., CUDA, NCCL, RDMA, NUMA, or GPU interconnects). Direct exposure ...

Airflow, Dagster or Argo. Experience building large-scale batch-inference or ML-platform systems, including model deployment and model registries. Knowledge of Spark, Databricks, Ray or comparable distributed-compute technologies. Experience with annotation or dataset-catalogue platforms, including metadata, provenance, versioning, lineage and quality control. Experience with Kubernetes, GPU infrastructure ...

Airflow, Dagster or Argo. Experience building large-scale batch-inference or ML-platform systems, including model deployment and model registries. Knowledge of Spark, Databricks, Ray or comparable distributed-compute technologies. Experience with annotation or dataset-catalogue platforms, including metadata, provenance, versioning, lineage and quality control. Experience with Kubernetes, GPU infrastructure ...

deployment to constrained edge/embedded compute, and the hardware-software trade-offs involved. Familiar with compute management and ML platform tooling - e.g. Kubernetes, Ray, Flyte, Docker, cloud (Azure), Python - and model-development/experiment workflow tooling. Proficient with AI Agents to accelerate execution: e.g. Cursor, Claude, Codex. Stays neutral ...

deployment to constrained edge/embedded compute, and the hardware-software trade-offs involved. Familiar with compute management and ML platform tooling - e.g. Kubernetes, Ray, Flyte, Docker, cloud (Azure), Python - and model-development/experiment workflow tooling. Proficient with AI Agents to accelerate execution: e.g. Cursor, Claude, Codex. Stays neutral ...

other inference‐speed work on large generative models. Reward models, offline RL, or closed‐loop evaluation of driving policies. Productionising research: Flyte/Ray/Spark‐style jobs, dataset lineage, training mix configuration; cloud GPU fleets (Azure/AWS/GCP) and distributed training. Strong publication record or contributions ...

Your Impact Provision and manage cloud-native AI/ML infrastructure utilising Kubernetes, Docker, and GPU orchestration frameworks (e.g., NVIDIA GPU Operator, Slurm, or Ray). Automate core platform infrastructure using Infrastructure as Code (IaC) tools like Terraform, Helm, and Ansible. Optimise GPU compute workloads, high-speed networking, and storage … Docker, Helm, KubeFlow, and service meshes (e.g., Istio). Hands-on experience with Terraform, Ansible, GitHub Actions, ArgoCD, or Jenkins. Experience with vLLM, Ray, MLflow, LangChain/LangSmith, DeepSpeed, or Hugging Face TGI. Solid background in AWS/GCP/Azure, Kubecost, and GPU cost optimisation techniques. Strong skills ...

tuning, lock-free programming, hardware-aware optimization, or FPGA-adjacent systems. Experience with ML/data tooling such as PyTorch, JAX, CUDA, GPU clusters, Ray, Kafka, Kubernetes, Spark, or comparable technologies. Understanding of market microstructure, backtesting pitfalls, transaction costs, and the operational needs of live quantitative strategies. Experience in environments ...

Qualifications Advanced degree (MSc/PhD) in Machine Learning, Computer Science, or related field Experience scaling machine learning training and data pipelines (e.g., using Ray or similar frameworks) Experience with cloud services and architectures (e.g. AWS, Azure) Experience with LLMs or generative AI systems (fine-tuning, prompting, evaluation) Knowledge ...

comfort in fast‐moving, under‐specified environments. Nice to have Distributed training experience (FSDP, DeepSpeed, Megatron, etc.). Cluster/orchestration experience (SLURM, Ray, Kubernetes, Kueue, Karpenter, Skypilot, etc.). Experience building reliable ML infrastructure, evaluation systems, or large‐scale data processing pipelines. Research experience in LLMs, agents, multimodal models ...

work on large generative models. AV/robotics/simulation; multi-sensor driving data (video, telemetry; LiDAR a plus). Productionising research: Flyte/Ray/Spark-style jobs, dataset lineage, training mix configuration. Reward models, offline RL, or closed-loop evaluation of driving policies. Cloud GPU fleets (Azure/ ...

TensorFlow) Demonstrated experience designing and analyzing controlled experiments (A/B testing, causal inference, hypothesis testing) Experience working with distributed data systems (Spark, Ray, Dask, or similar) Proficiency in SQL and working with large-scale structured datasets Experience building, deploying, and maintaining predictive models in production environments Strong understanding ...

building AI agents using orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar platforms Experience operating or integrating model serving platforms such as KServe, Ray Serve, or NVIDIA Triton Inference Server alongside other large language model serving stacks Familiarity with Amazon SageMaker JumpStart, SageMaker Endpoints, and Amazon Bedrock for managed … detection, toxicity filtering, and drift detection using open telemetry conventions Contributions to open-source large language model serving or inference projects, (vLLM, llm-d, Ray, KServe, Triton) #J-18808-Ljbffr ...

Rust, FastAPI or Node.js, PostgreSQL, Redis, REST and gRPC. Frontend: TypeScript, React, Next.js, and interfaces for streaming events and data visualization. Platform: Docker, Kubernetes, Ray, and durable workflow orchestration such as Temporal. Bonus: Rust, Python/TypeScript SDK development, or experience building tools for agents and ML workflows. Strong production ...

learning infrastructure, inference systems or ML performance optimisationExposure to large-scale batch processing, compute scheduling or resource prioritisationExperience with tools or frameworks such as Ray, Flyte or SparkExperience building shared libraries, developer tooling or internal platforms used across multiple teams#LI-HH1Wayve is committed to creating an inclusive interview experience. ...

equivalent industry experience in Computer Science, Engineering or a related field- Experience with LLM serving frameworks such as vLLM, TGI, Triton or Ray Serve- Knowledge of GPU scheduling, partitioning or resource-sharing technologies- Experience with distributed AI inference, model parallelism or large-scale AI infrastructure- Familiarity with networking concepts relevant ...

monitoring, management, and efficiency of large compute platforms. You will collaborate with cross-functional teams, deploy on Kubernetes, and work with Slurm, LSF, Spark, Ray, and Symphony in cloud environments to deliver scalable, secure technologies #J-18808-Ljbffr ...

containerized ecosystems. Preferred Technical Exposure Experience building infrastructure for simulation software, virtual testbeds, or automated control systems. Experience with Reinforcement Learning tools such as Ray RLlib Familiarity with high-efficiency runtime environments or specialized hardware acceleration toolchains. Background in remote system provisioning, telemetry transport protocols (such as messaging queues ...

these — as long as you're open to learning, Backend: Python Frontend: TypeScript and React Deployment: Kubernetes Infrastructure: GCP Machine learning: PyTorch, CUDA, Ray Why Encord Competitive salary, commission, and meaningful equity in a high-growth startup Strong in-person culture — most of the team works from our London office ...

long as you\'re open to learning, please apply. Backend: Python Frontend: TypeScript and React Deployment: Kubernetes Infrastructure: GCP Machine learning: PyTorch, CUDA, Ray Why Encord Competitive salary, commission, and meaningful equity in a high-growth startup Strong in-person culture — most of the team works from our London office ...

data structures, algorithms, networking, OS, concurrency, and system design. Experience running compute at cluster scale: job scheduling, resource management, retries, and reliability. Slurm, Kubernetes, Ray, Spark, or custom internal schedulers all count. Proven data‐engineering experience: schema design, storage formats, compression, I/O trade-offs, and pipelines processing hundreds ...

processing across distributed cloud warehouses/storage via container orchestration (e.g. Kubernetes), High-Performance GPU Compute (e.g. Slurm), or distributed compute frameworks (e.g. Spark, Ray). Contribute to an engineering culture that values maintainability, testing, robust system design, and deep collaboration, but allows flexibility for rapid prototyping and responsiveness ...

experience in a data engineeringStrong proficiency in Python and RustHands-on experience with AWS cloud, Linux, and Docker. Expertise in distributed computing frameworks (Ray, Dask, or Apache Arrow).Strong analytical skills, with the ability to handle large-scale, high-frequency data. Solid understanding of ETL pipelines, data modelling, and database ...

strategy software to minimise trading latency and scale effectively. Our ML stack is Python based and utilises modern ML libraries and tooling including Polars, Ray, Plotly etc. The ideal candidate will have a strong software engineering background, with broad experience across a range of topics related to general high performance ...

complex optimization problems such as ranking systems or multi‐objective decision‐making You bring hands‐on experience with PyTorch and distributed systems such as Ray or similar frameworks You understand experimentation deeply and can design reliable tests in environments with interacting metrics You are able to analyze results using approaches ...