4 of 4 Ray Jobs in Central London

justify"- -Programming Skills: Proficiency in Python, data analytics, deep learning (Scikit-learn, Pandas, PyTorch, Jupyter, pipelines), and practical knowledge of data tools like Databricks, Ray, Vector Databases, Kubernetes, and workflow scheduling tools such as Apache Airflow, Dagster, and Astronomer. -GPU Computing: Familiarity with GPU computing, both on-premises and on More ❯

navigating hybrid infrastructure : some workloads will be on-prem, others cloud (large GPU clusters). Familiarity with distributed systems and container orchestration (e.g., Kubernetes, Ray). Experience working client-facing or in cross-functional teams — ideally within pharma/life sciences .1 A “get stuck in” attitude — this is a More ❯

business units to architect and optimise large-scale, compute-intensive work flows spanning global locations. You will work with cutting-edge platforms such as Ray and YellowDog, driving the integration and support of distributed computing solutions to enhance performance and scalability in complex environments. Key Responsibilities: Partner with business teams … on distributed platforms. Provide architectural and technical leadership in the design and development of distributed systems. Design, implement, and manage distributed computing solutions using Ray and YellowDog. Required Skills & Experience: Bachelor's degree in Computer Science, Engineering, or a related field. Deep understanding of loosely and tightly coupled workloads. Hands … AWS, Azure, or Google Cloud Platform (GCP). Proven experience working with large-scale systems (1000+ nodes, 10,000+ tasks). Advanced expertise in Ray for machine learning pipelines, hyperparameter tuning, and distributed execution. Strong programming skills in Python and experience with Conda. Proficiency with Docker and Kubernetes for containerisation More ❯

Architect and optimise large-scale compute-intensive workloads spanning significant numbers of nodes and concurrent tasks Design, build, and manage systems with tools like Ray and YellowDog Optimise application performance on distributed platforms Provide architectural guidance on distributed computing design and development Drive efficiency and scalability across the platform, with … i.e. Yellowdog Cloud platform proficiency (any provider) Experience with large scale systems (1k+ Nodes, 10k+ tasks) Experience monitoring/troubleshooting a distributed environment Advance Ray experience for ML pipelines, tuning, distributed execution Python and Conda proficiency Docker + Kubernetes experience Knowledge of networking (TCP/IP, UDP/IP, LAN More ❯