16 of 16 Ray Jobs in the UK

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 Commodities, energy, oil, gas or power markets. VaR or risk engine development. Pricing libraries or quantitative analytics platforms. Distributed computing technologies such as Ray, Dask or Spark. Market data pipelines or time-series data platforms. Near real-time or intraday risk systems. Containerisation technologies such as Docker or Kubernetes. ...

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 ...

optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs. Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections. Public work. Non-trivial systems programming projects, open-source ...

platform. You'll be writing the distributed Python that turns market data and curves into risk at scale on AWS and Ray, with enough functional understanding of commodities to challenge the requirement, not just implement it. Context Pricing & Risk Technology owns the chain that turns raw market data into curves … turns them into risk, at the scale and speed the business needs. Both are Python, both run on AWS, and both increasingly lean on Ray for distributed compute — parallelising curve construction across large hierarchies and running risk/scenario grids that don't fit on one box. This role ...

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 ...

Engineering: You are an engineer first. Deep proficiency in Python/C++, experience with low-latency inference systems, and familiarity with distributed computing frameworks (Ray, Spark, Flink). You write code that survives in production, not just notebooks. The Full ML Lifecycle: Expertise in end-to‐end system design: from ...

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 ...

#Python backend expertise (e.g. FastAPI or similar) and this hire will have significant ownership Data platform: #LanceDB, #DataFusion, #Arrow, Parquet, Iceberg Training infra: #PyTorch, Ray, multi-node distributed setups Inference: Triton, model ensembles, production reliability Multimodal pipelines: TB-scale video, embedding, and #metadata #datasets Qualifications It's a hybrid role ...

intersection of machine learning, product, and real-world constraints. The team is supported by a state-of-the-art ML Ops ecosystem (AWS, Encord, Ray, PyTorch Lightning, Weights & Biases) and partners closely with product and engineering to ship models into production at scale. This is applied research in its truest ...

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 ...

large-scale inference and data pipelines, focusing on KV cache management, heterogeneous memory scheduling, and high-throughput inference serving using frameworks like vLLM, Ray Serve, and modern PyTorch Distributed systems. Scalable Model Serving Infrastructure: Develop and evaluate frameworks that enable efficient multi-tenant, low-latency, and fault-tolerant AI serving … knowledge of distributed systems, operating systems, machine learning systems architecture, Inference serving, and AI Infrastructure. Hands-on experience with LLM serving frameworks (e.g., vLLM, Ray Serve, TensorRT-LLM, TGI) and distributed KV cache optimization. Proficiency in C/C++, with additional experience in Python for research prototyping. Solid grounding ...

depth profiling and performance tuning of inference pipelines, focusing on KV cache management. Develop low-latency, fault-tolerant AI serving frameworks using vLLM, Ray Serve, and PyTorch Distributed. Research and prototype novel techniques for cache sharing, data locality, and resource orchestration. Translate innovative designs into publishable contributions at top-tier … systems, or related field. Strong knowledge of Distributed Systems, OS internals, and Machine Learning systems architecture. Hands-on experience with LLM serving frameworks (vLLM, Ray Serve, TensorRT-LLM, or TGI). Proficiency in C/C++ for systems development and Python for research prototyping. Solid grounding in distributed algorithms, load ...

least one major ML framework (PyTorch, JAX) and quick to pick up others Familiar with distributed training and inference tooling: DeepSpeed, FSDP, Megatron, ZeRO, Ray Engineering discipline: code that's readable, robust, and maintainable Experience optimising for GPU constraints: quantisation, mixed precision, memory Comfortable taking ownership of ambiguous problems from … contributions to ML or systems libraries Scientific computing, compiler, or GPU kernel experience Multimodal or diffusion model background Large-scale data processing: Arrow, Spark, Ray Why join At a big company, ML work gets absorbed into a machine. Here, your systems are the product. You'll work closely with research ...

etc.). Extensive experience with PyTorch, NumPy, Pandas. Hands‐on experience in implementing distributed/multi‐threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc.). Able to communicate technical information and ideas at all levels; convey information clearly and create trust with stakeholders. Preferred qualifications, capabilities … skills Experience designing/implementing pipelines using DAGs (e.g., Kubeflow, DVC, Ray). Experience with big data technologies (e.g., Spark, Hadoop). Have constructed batch and streaming microservices exposed as REST/gRPC endpoints. Familiarity with GraphQL. We recognize that our people are our strength and the diverse talents they ...

depth profiling of large-scale inference pipelines, specifically focusing on KV cache management and heterogeneous memory scheduling. AI Serving: Optimising high-throughput frameworks (vLLM, Ray Serve, PyTorch Distributed) to ensure low-latency, multi-tenant performance. Research Leadership: Contributing to top-tier venues (OSDI, NSDI, EuroSys, MLSys) and driving those innovations … Stack: Strong proficiency in C/C++ for systems work, with Python for rapid prototyping. Expertise: Hands-on experience with LLM serving frameworks ( vLLM, Ray Serve, TensorRT-LLM ) and distributed algorithms. Mindset: A solid grounding in systems research methodology and performance profiling tools. The "Value Add" (Desired): A PhD focused ...