10 of 10 Ray Jobs in the City of London

engineering at scale . Expertise in Python (plus C Java a bonus), and familiarity with ML frameworks like PyTorch, TensorFlow, Scikit-learn . Experience building production-grade pipelines (Spark, Ray, Kafka, Dask, Kubernetes, cloud). Ability to handle large, noisy datasets — financial or otherwise — and turn them into production-ready models. Curiosity, pragmatism, and the mindset to solve problems where More ❯

in Deep Learning, including training, evaluation, and optimisation. Strong grounding in mathematics, statistics, and data analysis. Experience working in Agile environments. Familiarity with technologies such as AWS, GCP, Kubernetes, Ray Serve, and Kubeflow is desirable. ---------------------------------------- Professional Values Growth: Demonstrates curiosity, adaptability, and continuous learning. Accountability: Takes ownership and delivers to a high standard. Innovation: Embraces experimentation and emerging technologies to More ❯

CD pipelines for AI deployments (Github Actions, MLFlow, ZenML, or similar). Deep understanding of containerisation and orchestration tools (Docker, Kubernetes). Desirable Experience deploying AI inference engines (vLLM, Ray Serve, Triton). Familiarity with observability tools for LLMs (TruLens, Helicone, LangSmith). Understanding of AI safety and reliability frameworks (Guardrails AI). This is an exciting opportunity to help More ❯

and communication skills. NICE TO HAVE: Hands-on experience with LLMs and Natural Language Processing (NLP) , including fine-tuning or prompt engineering. Familiarity with distributed computing or parallel processing (Ray, Spark, etc.). Experience deploying models in production environments (Docker, cloud services). Exposure to data engineering or working alongside data pipeline teams. A genuine passion for AI innovation and More ❯

and communication skills. NICE TO HAVE: Hands-on experience with LLMs and Natural Language Processing (NLP) , including fine-tuning or prompt engineering. Familiarity with distributed computing or parallel processing (Ray, Spark, etc.). Experience deploying models in production environments (Docker, cloud services). Exposure to data engineering or working alongside data pipeline teams. A genuine passion for AI innovation and More ❯

and serving. Experience managing containerised 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/GPU workloads. Excellent people leadership and More ❯

teams. Nice to Have Experience integrating multi-omic or imaging data with clinical outcomes. Knowledge of cloud platforms (AWS, GCP, or Azure) and distributed computing tools (PySpark, Dask, or Ray). Familiarity with reinforcement learning or causal ML for adaptive interventions. Why Apply Join a company combining scientific excellence, AI innovation, and real-world health impact. Work with world-leading More ❯

Applied Scientist team consists of 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 Overview : We are looking for More ❯

Applied Scientist team consists of 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 Overview : We are looking for More ❯

Research Engineer (Data Infra/ML) London (Hybrid) Can you build & optimize distributed ML pipelines with Ray or Spark? Do you love speeding up cloud infra (Kubernetes, Docker, CI/CD)? Excited to build the data backbone for large-scale ML training? We're a tier 1 VC-backed start-up, developing hyper-realistic 3D simulations using AI. Our customers … productionize PyTorch models and streamline ML workflows. Develop tools that make data discoverable, reusable, and reliable throughout the ML lifecycle. You Strong Python skills and experience with distributed systems (Ray, Spark, Flyte, Dask). Hands-on with cloud, Kubernetes, and distributed training (Ray, PyTorch DDP, Horovod). Familiar with dataset versioning and experiment tracking (DVC, MLflow). Bonus Points Experience More ❯