26 to 50 of 92 Ray Jobs

technologies.Strong 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 to lead ...

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 ...

multi‐modal representation learning. Technical Stack: Mastery of Python and deep learning frameworks (TensorFlow, PyTorch) alongside hands‐on experience with distributed computing (Spark, Ray) and cloud infrastructure (AWS/GCP). Graph Neural Networks (GNNs): Strong experience applying GNNs, knowledge graphs, or graph embeddings to map complex relations between travel ...

retrieval, and multi-modal representation learning.Technical Stack: Mastery of Python and deep learning frameworks (TensorFlow, PyTorch) alongside hands-on experience with distributed computing (Spark, Ray) and cloud infrastructure (AWS/GCP).Desired:Graph Neural Networks (GNNs): Strong experience applying GNNs, knowledge graphs, or graph embeddings to map complex relations between ...

multi-modal representation learning. Technical Stack: Mastery of Python and deep learning frameworks (TensorFlow, PyTorch) alongside hands-on experience with distributed computing (Spark, Ray) and cloud infrastructure (AWS/GCP). Graph Neural Networks (GNNs): Strong experience applying GNNs, knowledge graphs, or graph embeddings to map complex relations between travel ...

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) ABOUT US J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations ...

patternsExperience building AI agents using orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar platformsExperience operating or integrating model serving platforms such as KServe, Ray Serve, or NVIDIA Triton Inference Server alongside other large language model serving stacksFamiliarity with Amazon SageMaker JumpStart, SageMaker Endpoints, and Amazon Bedrock for managed model … hallucination detection, toxicity filtering, and drift detection using open telemetry conventionsContributions to open-source large language model serving or inference projects, (vLLM, llm-d, Ray, KServe, Triton)J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy ...

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 ...

network 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 operating real ...

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 ...

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 ...

level programming (C/C++, Rust, CUDA) Direct contributions to a major inference/serving engine or similar ML infra project (e.g. PyTorch, Ray) Strong grasp of LLM inference internals (KV caching, batching, quantisation) Experience upstreaming to active OSS communities Hardware accelerator optimisation experience (GPU/TPU/etc.) Genuine ...

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 ...

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 ...

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 ...

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 ...

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. ...

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 ...