8 of 8 Ray Jobs in London

infrastructure, model deployment, distributed compute, GPU optimization, and MLOps architecture. Willingness to learn new parts of the ML tech stack – Python, PyTorch, Docker, Kubernetes, Ray, Weights & Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and model serving frameworks. Fluency in life sciences or drug discovery is a plus. Working Location & Compensation: This ...

Excitement to learn parts of our ML tech stack that you might not already know. Our current ML infrastructure includes: Python, PyTorch, Docker, Kubernetes, Ray, Weights & Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and various model serving frameworks. Fluency in life sciences or drug discovery is a plus but not required ...

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 ...

orchestration tools (Temporal, Airflow, Kubeflow, or similar). Cloud platform expertise (AWS required, Azure & GCP beneficial). Experience with data processing frameworks (Spark, Athena, Ray, or similar). Experience with systems having ML Engineering and MLOps aspects. Proven track record of leading architectural transformations in growing companies. Excellence in technical ...

including 2D and 3D geometry Experience with cloud services and architectures (e.g. AWS, Azure) Proficiency with modern deep learning libraries and frameworks (PyTorch, HuggingFace, Ray) Excellent written documentation skills to document code, architectures, and experiments Preferred Qualifications Experience working with distributed systems Knowledge of the design, manufacturing, AEC, or media ...

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 ...

experience, with a strong record of shipping production systems in Python (and ideally C++). Deep experience with ML training infrastructure at scale (e.g. Ray, Slurm) including GPU orchestration, feature stores, experiment tracking, and model serving. Experience with deep learning model frameworks ...

pipelines for video indexing and processing (face detection, quality assessment, tracking) Improve training performance across single and multi-node setups using PyTorch and Ray Build evaluation and experimentation systems (Parquet/Iceberg) for model output analysis Own model versioning, lifecycle management, and promotion to production Optimise inference pipelines using Triton … pipeline performance trade-offs: I/O, compute, batching, memory layout Hands-on PyTorch experience: training pipelines, data loading, preprocessing Practical distributed systems experience (Ray, DDP, or similar) Experience handling TB-scale or high-throughput data pipelines Familiarity with columnar formats: Arrow, Parquet, Iceberg Nice to Have Exposure to video ...