8 of 8 Ray Jobs in Central London

strong portfolio of research publications (ICML, NeurIPS, KDD, etc.) or patents related to applied AI. Extensive experience with distributed computing frameworks (e.g., Spark, Ray) for large-scale model training and inference. ...

data quality. Strong proficiency in one the programming languages, such as Python, Java, Scala, C++, etc. Experience with distributed computing frameworks such as Ray, Spark, and MapReduce. Attention to detail and a strong commitment to data integrity. Excellent problem-solving skills and ability to work in a fast-paced environment. ...

workloads (Docker, Kubernetes, Kubeflow, etc.) and integrating with CI/CD tools (Jenkins, GitHub Actions, GitLab CI). Familiarity with distributed computing frameworks (Spark, Ray, TensorFlow Distributed, PyTorch Distributed). Strong understanding of monitoring, logging, and observability for large-scale ML systems. Experience in cost optimisation for compute/ ...

workloads (Docker, Kubernetes, Kubeflow, etc.) and integrating with CI/CD tools (Jenkins, GitHub Actions, GitLab CI). Familiarity with distributed computing frameworks (Spark, Ray, TensorFlow Distributed, PyTorch Distributed). Strong understanding of monitoring, logging, and observability for large-scale ML systems. Experience in cost optimisation for compute/ ...

PyTorch), from prototype to deployment. We are seeking someone with the following experience: Strong Python programming and solid data engineering fundamentals. Hands-on with Ray, Spark, Databricks, or similar distributed compute frameworks. Experience managing cloud infrastructure, Kubernetes, and CI/CD pipelines. Familiar with dataset versioning, orchestration, and experiment tracking ...

about twenty machine learning scientists. The team is supported by an ML Ops team that provides state-of-the-art tooling (including AWS, Encord, Ray, PyTorch Lightning and Weights & Biases). The Applied Science team works closely with product engineering to deploy models to serve our worldwide customer base. Position ...

about twenty machine learning scientists. The team is supported by an ML Ops team that provides state-of-the-art tooling (including AWS, Encord, Ray, PyTorch Lightning and Weights & Biases). The Applied Science team works closely with product engineering to deploy models to serve our worldwide customer base. Position ...

source language models. Developing complete agent workflows , from event triggers to model endpoints to user-facing integrations. Expect to work with systems like n8n, Ray Serve, vector stores (pgvector, cloud search), and messaging surfaces such as Teams or similar platforms. Building and operating cloud infrastructure using container orchestration and infrastructure ...