21 of 21 Permanent Ray Jobs in the UK

levels; convey information clearly and create trust with stakeholders. Preferred qualifications, capabilities, and skills Experience designing/implementing pipelines using DAGs (e.g. Kubeflow, DVC, Ray) Experience of big data technologies Have constructed batch and streaming microservices exposed as REST/gRPC endpoints Experience with container orchestration tools (e.g. Kubernetes, Helm ...

impact-scalability tradeoff into account. Some skills that will make you stand out: Apache Spark, Iceberg, Kafka, dbt Scikit-Learn, XGBoost, PyTorch, MLFlow,, GraphFrames, Ray AWS (S3, EMR, SageMaker, Lakeformation), Terraform, Docker, GitHub CI/CD Knowledge Graphs (+ RAG), graph ML, probabilistic programming, A/B testing Additional Information ...

testing, code review, CI/CD). Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models. Experience with model serving frameworks (vLLM, TensorRT, Ray). Experience with A/B testing and experimentation frameworks for AI features. Experience with model observability tools (LangSmith, W&B, MLflow). Our Commitment ...

experience with cloud-native ML infrastructure platforms Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models Experience with model serving frameworks (vLLM, TensorRT, Ray) Experience with A/B testing and experimentation frameworks for AI features Contributions to open-source ML projects or research publications Experience with model observability ...

strong background in mathematics and statistics Strong proficiency in programming languages such as Python, with experience in libraries like numpy, pytorch, polars, pandas, and ray Demonstrated experience in designing and implementing end-to-end machine learning pipelines, including data preprocessing, model training, deployment, and monitoring Understanding of and experience with ...

strong background in mathematics and statistics Strong proficiency in programming languages such as Python, with experience in libraries like numpy, pytorch, polars, pandas, and ray Demonstrated experience in designing and implementing end‐to‐end machine learning pipelines, including data preprocessing, model training, deployment, and monitoring Understanding of and experience with ...

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 ...

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 ...

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 ...

infrastructure details. Nice to Have Experience managing physical data centres, co‐location facilities, or hybrid infrastructure environments. Working knowledge of ML orchestration frameworks (e.g., Ray, Slurm, Kubeflow). Background in media pipelines, VFX tooling, or media compliance standards (MPA, ISO 27001). Prior experience working in a hybrid startup/ ...

Claude Code, Codex, Kimi Code, Pi Agent, Droid, or similar agentic coding systems as a development surface Experience with GPU clusters on Kubernetes, Slurm, Ray, custom schedulers, or cloud GPU orchestration NCCL, UCX, NVSHMEM, RDMA, InfiniBand, RoCE, or EFA Rust, C++, CUDA, Go, or systems‐level performance work Why White ...

Experience handling time-series challenges such as timestamp precision, sequence gaps, duplicate and out-of-order events. Experience with distributed processing (Spark, Polars, Dask, Ray or similar). Experience with Kafka or similar streaming technologies. Familiarity with PostgreSQL, ClickHouse, Snowflake, Databricks, kdb+ or similar. Docker, Linux, Git and CI/ ...

Engineering: You are an engineer first. Deep proficiency in Python/C++, experience with low-latency inference systems, and familiarity with distributed computing frameworks (Ray, Spark, Flink). You write code that survives in production, not just notebooks. The Full ML Lifecycle: Expertise in end-to‐end system design: from ...

Experience building AI agents using frameworks such as LangChain, CrewAI, LangGraph, or similar orchestration platforms Experience operating or integrating serving platforms such as KServe, Ray Serve, NVIDIA Triton Inference Server, Text Generation Inference (TGI), alongside vLLM/llm‐d Familiarity with Amazon SageMaker JumpStart, SageMaker Endpoints, and Amazon Bedrock … monitoring (e.g., hallucination, toxicity, drift detection) and tracing via OpenTelemetry conventions Contributions to open‐source LLM serving or inference projects (e.g., vLLM, llm‐d, Ray, KServe, Triton) #J-18808-Ljbffr ...

Demonstrate advanced proficiency in Python with extensive experience building production applications and services Experience with modern cluster compute technologies (e.g., Kubernetes, Spark, Hadoop Yarn, Ray, Airflow) Experience leading work in a formal software development environment in collaboration with other engineers Strong experience building platforms on the Linux operating system Self ...

capabilities Advanced proficiency in Python, with extensive professional experience in building production applications and services Experience of modern cluster compute - Kubernetes, Spark, Hadoop Yarn, Ray, Airflow are some of the technologies we use Experience leading work in a formal software development environment alongside other engineers in a collaborative manner Strong ...

deliver scalable, production-grade Python services and APIs for research and operations. The role emphasizes modern cluster compute technologies (Kubernetes, Spark, Hadoop Yarn, Ray, Airflow) and strong proficiency in Python. A background in mathematics or computing is advantageous. #J-18808-Ljbffr ...

engineers across the firm. Ideal candidates have deep Python expertise, hands-on experience with modern cluster compute technologies (Kubernetes, Spark, Hadoop/Yarn, Ray, Airflow) and a track record of delivering scalable infrastructure in a multi-user #J-18808-Ljbffr ...

depth profiling and performance tuning of inference pipelines, focusing on KV cache management. Develop low-latency, fault-tolerant AI serving frameworks using vLLM, Ray Serve, and PyTorch Distributed. Research and prototype novel techniques for cache sharing, data locality, and resource orchestration. Translate innovative designs into publishable contributions at top-tier … systems, or related field. Strong knowledge of Distributed Systems, OS internals, and Machine Learning systems architecture. Hands-on experience with LLM serving frameworks (vLLM, Ray Serve, TensorRT-LLM, or TGI). Proficiency in C/C++ for systems development and Python for research prototyping. Solid grounding in distributed algorithms, load ...

autoencoders etc.) Extensive experience with PyTorch, NumPy, pandas Hands‐on experience in implementing distributed/multi‐threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc.) Able to communicate technical information and ideas at all levels; convey information clearly and create trust with stakeholders. Preferred qualifications, capabilities … skills Experience designing/implementing pipelines using DAGs (e.g. Kubeflow, DVC, Ray) Experience of big data technologies (e.g. Spark, Hadoop) Have constructed batch and streaming microservices exposed as REST/gRPC endpoints Familiarity with GraphQL Level of experience Senior (5+ years of experience) Tagged as: Big Data, Industry, Machine Learning ...

autoencoders etc.) Extensive experience with pytorch, numpy, pandas Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc.) Able to communicate technical information and ideas at all levels; convey information clearly and create trust with stakeholders. Preferred qualifications, capabilities … skills Experience designing/implementing pipelines using DAGs (e.g. Kubeflow, DVC, Ray) Experience of big data technologies (e.g. Spark, Hadoop) Have constructed batch and streaming microservices exposed as REST/gRPC endpoints Familiarity with GraphQL #CIBAppliedAI ABOUT US J.P. Morgan is a global leader in financial services, providing strategic advice ...