268 jobs for “data science”
PhysicsX London
Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, forecasting, anomaly detection) into customer production environments, building scalable training and inference pipelines with Python, PyTorch, Kubeflow and fastAPI. Leads scoping and architecture, mentors engineers, and travels to customer sites roughly 3-4 weeks per quarter.
PhysicsX New York, United States
Builds and automates high-fidelity electromagnetic models across low- and high-frequency applications, from chip and package design to EMI/EMC, using tools like HFSS, Maxwell and CST. Works at the intersection of CAE and data science, generating simulation data to train ML surrogates, collaborating with customers and mentoring junior engineers.
NVIDIA Bristol
Research and develop techniques to GPU-accelerate deep learning, machine learning and other AI workloads, working with technical experts from industry and academia to analyse and optimise complex AI and HPC algorithms on modern CPU and GPU architectures. The intern also publishes optimisation techniques via developer blogs or conferences and helps influence next-generation hardware, software and programming models.
NVIDIA Zürich, Switzerland
Research and develop techniques to GPU-accelerate deep learning, machine learning and other AI workloads, working with technical experts to analyse and optimise complex AI and HPC algorithms on modern CPU and GPU architectures. The role also involves publishing optimisation techniques via developer blogs or conferences and influencing next-generation hardware and software design.
University of Exeter Exeter £21,805 pa
Develops a resource-aware AI framework for 2D and 3D medical imaging that adapts computation to case complexity, clinical risk and available hardware. Investigates where unnecessary computation occurs, builds a configurable model family, and validates it on population-scale abdominal MRI (UK Biobank) and near-patient dermatology imaging, with safety assessed across patient subgroups.
Databricks London
Serve as the trusted technical ML and AI expert for enterprise customers, architecting production-grade ML and GenAI workloads on the Databricks platform. The role spans RAG architectures, agentic systems, MLOps lifecycle management, and cloud-native integration, while mentoring colleagues and influencing the product roadmap. It combines deep hands-on engineering with customer-facing technical sales support.
Databricks London
Owns the core write-path operations for Delta Lake, the open-source storage layer behind the Databricks Lakehouse, driving performance innovations like Low Shuffle Merge and Deletion Vectors. Designs and maintains high-QPS, low-latency backend systems at massive scale, with technical leadership, mentoring and cross-team project planning.
Aioi Nissay Dowa Europe Oxford £81,322 – £112,488 pa
This role involves leading research in privacy-preserving generative AI, focusing on techniques like differential privacy, federated learning, and secure inference. The scientist will design and validate machine learning models, translate research into deployable systems, and contribute to publications and collaborations within a multi-partner environment. Emphasis is placed on responsible AI, reproducibility, and mentoring junior team members.
University of Surrey Guildford £21,805 pa
Develops next-generation federated machine unlearning algorithms that deliver verifiable, robust erasure without harming model performance or participant privacy. Research spans certified unlearning with formal guarantees, robustness to adversarial relearning, federated-specific benchmarks, and scalable unlearning for foundation models, supervised by Dr Pedro Porto Buarque de Gusmao and Dr Frank Guerin.
University of Surrey Guildford £21,805 pa
Develops a computational framework combining computer vision, graphics and machine learning to speed up and improve cinematic camera lens design and simulation. The work covers forward simulation of candidate lens configurations and inverse design of lens architectures against imaging or perceptual objectives, using differentiable rendering and data-driven models. Based at the Vision, Speech and Signal Processing group under Dr Marco Volino and Dr Jean-Yves Guillemaut, with close industry collaboration.
Scale AI London
New grad engineers own customer-facing work from day one, shipping products, speaking directly with customers and iterating quickly. The Global Public Sector team builds custom AI applications, training data pipelines for national LLMs, evaluation infrastructure, agentic AI tooling and fraud-detection systems, working in Python, TypeScript, React and MongoDB.
Scale AI London
Builds full-stack AI applications directly alongside public sector clients, scoping requirements, gathering feedback and shipping production-grade systems. Work spans React and TypeScript front ends, Python/Node backends, and cloud infrastructure on AWS, Azure or GCP, with a focus on reusable platform features and deploying into customer environments.
Causaly London
Design and ship ML/AI solutions end-to-end, from data exploration to deployment and monitoring, choosing between classical ML, NLP, LLM and agentic approaches. Work closely with product, design and engineering on user-facing AI features, champion model and data quality, and mentor junior engineers in a biomedical knowledge platform team.
University of Exeter Exeter £21,805 pa
Develops explainable, trustworthy machine-learning models for early dementia detection, integrating multimodal data such as neuroimaging, cognitive assessments, genetics and biomarkers. Uses latent knowledge-enhanced representations, vision-language agentic frameworks and uncertainty quantification, with clinician evaluation of bias and fairness. Validated through a longitudinal multimodal prediction and explanation system.
Isomorphic Labs Cambridge, United States
Lead computational drug design programs from initiation through to clinical candidate selection, applying AI-first approaches and shaping the company's ML drug design engine. Work cross-functionally with ML, biology and chemistry teams, with optional people management of 2-3 computational chemists.
