3 of 3 Permanent Ray Jobs in the City of London

role involves collaborating across multiple 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 to embed distributed computing into … Optimise applications for high performance 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-on experience with HPC platforms … in cloud platforms such as 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 and orchestration. More ❯

training and inference). Comfort 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 team of doers, not just More ❯

fit share your CV! Role: 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 a focus on ML pipeline … Job/Resource scheduling experience 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/WAN) Identify and access More ❯