26 to 50 of 66 Ray Jobs in London

such as DDP or FSDP.Experience with observability tools such as Prometheus, Grafana, Datadog, or OpenTelemetry.Experience with data pipeline orchestration tools such as Airflow, Flyte, Ray, Metaflow, or Argo Workflows.Experience with containerisation and infrastructure tooling such as Docker, Kubernetes, or Terraform.Experience profiling or optimising ML systems, for example using NVIDIA Nsight.Understanding ...

background in AWS cloud services, high-performance computing, distributed systems, containerised environments, and infrastructure automation. Experience with workload orchestration technologies such as Slurm, Kubernetes, Ray, or equivalent distributed compute frameworks. Demonstrated success bridging research and production environments, enabling rapid experimentation while maintaining operational excellence, governance, security, and reliability. Proven ability ...

FSDP. Experience with observability tools such as Prometheus, Grafana, Datadog, or OpenTelemetry. Experience with data pipeline orchestration tools such as Airflow, Flyte, Ray, Metaflow, or Argo Workflows. Experience with containerisation and infrastructure tooling such as Docker, Kubernetes, or Terraform.Experience profiling or optimising ML systems, for example using NVIDIA Nsight. Understanding ...

caching, or 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 to open ...

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 ...

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 ...

model 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 and adapts ...

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/ ...

inference-speed 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/AWS/ ...

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 ...

scale. The day‐to‐day work combines data preprocessing, model training (PyTorch, TensorFlow), evaluation against business or research metrics, and production deployment (FastAPI, Triton, Ray) with monitoring. The role increasingly overlaps with MLOps (model deployment, monitoring, retraining infrastructure) and applied ML research, taking new techniques from papers to production. Generative ...

documentation skillsPreferred QualificationsAdvanced degree (MSc/PhD) in Machine Learning, Computer Science, or related fieldExperience 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 ...

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 ...

hands-on experience operating ML training and serving workflows in production environments Experience with distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP or Ray Train Experience operating experiment tracking systems and model registry systems such as MLflow, Weights and Biases or equivalent Solid understanding of cloud infrastructure ...

hands‐on experience operating ML training and serving workflows in production environments Experience with distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP or Ray Train Experience operating experiment tracking systems and model registry systems such as MLflow, Weights and Biases or equivalent Solid understanding of cloud infrastructure ...

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 ...

cost-efficiency.Your ImpactProvision 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 for efficient … Kubernetes (K8s), 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 in Python, Bash ...

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. ...

testable, well‐documented and maintainable code, version control and code reviews; Have experience with distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP or Ray Train; Have experience optimising models for inference, including quantisation, distillation and pruning; Nice to have: experience with biomedical data modalities such as genomics, multi‐omics ...

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 ...

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: the team works from our London office 4+ days ...