Artificial Intelligence Jobs

PhysicsX · Singapore

Lead the deployment of ML models and engineering surrogates—deep learning on CAE/CFD/FEA data, time-series forecasting, anomaly detection, and optimisation—into customer production environments. Own scoping, architecture, and success metrics for data/ML systems built with Python, PyTorch, Pandas, fastAPI, Scipy, and Kubeflow across cloud and on-prem. Mentor engineers and data scientists while travelling 3-4 weeks per quarter to customer sites worldwide.

CrowdStrike · Ireland

Security researcher role on CrowdStrike Intelligence's Advanced Research Team, performing vulnerability research and exploit development across a wide range of targets. Day-to-day work includes analysing vendor patches, identifying exploits in large file collections, reverse engineering, and building automated data collection and analysis tooling, alongside publishing research and supporting other intelligence teams.

CrowdStrike · United Kingdom

Join the Advanced Research Team to conduct vulnerability research and exploit development, analysing vendor patches to identify vulnerabilities and build proof-of-concept exploits. The role also involves reverse engineering, investigating 0-day and n-day exploitation, and developing automated tooling to process and analyse data at scale, with results published on the blog or at conferences.

Wayve · London

Owns the technical architecture and roadmap for LiDAR and cross-sensor 3D sensing, developing calibration, registration, temporal alignment and motion compensation methods. Blends algorithm research with production deployment in C++/Python, providing technical leadership and mentorship across the 3D sensing stack.

Wayve · London

Lead functional safety activities for an end-to-end autonomous driving system, building Item Definitions, running HARA/FMEA/FTA analyses, and shaping safety concepts and architectures across vehicle platforms and third-party systems. Define verification and validation, including vehicle-level testing, and construct structured safety cases with claims, arguments and evidence to support certification.

NVIDIA · Reading

Lead the definition and implementation of hardware and software security architecture for next-generation networking products, taking a hands-on role in microarchitectural modelling, validation and standards work. Collaborate with internal hardware, software and research teams plus external partners on open-source and IO interfaces such as I2C, I3C, SPI, USB and PCIe, and engage with customers on security threats.

NVIDIA · United Kingdom

Lead security research, architecture and implementation for next-generation NVIDIA Networking products, taking a hands-on role defining hardware and software security solutions. Work across hardware, software, research and product teams, and with external partners, on IO interfaces, boot architecture, RISC-V, root-of-trust and secure platforms.

Faculty · London

Owns and develops product and service design as a core discipline within the Applied AI division, delivering billable client engagements and anchoring project deliverables. Builds executive relationships with client leadership, defines skill and progression frameworks, and partners with business development to shape multidisciplinary teams. Balances strategic delivery with building an entrepreneurial design function.

Faculty · London

Lead high-impact AI safety projects for Frontier Labs, inventing novel evaluation frameworks and designing robust software architectures. Mentor technical team members while partnering with commercial teams to scope work and translate complex problems into rigorous frameworks. Requires Python, NumPy, Pandas and ideally evaluation tooling like Inspect AI.

Helsing · London

Builds computer vision models for object detection, recognition, classification and tracking, extending state-of-the-art architectures and running large-scale multi-GPU training on sensor data. Optimises models for real-time performance on constrained target hardware and collaborates with research, engineering and product teams to shape perception roadmaps.

Morson Edge · West Yorkshire · £170,000 – £180,000 pa

A senior leadership role owning the data and AI strategy, roadmap and technical direction across the organisation. Day to day it spans hands-on work with generative AI, LLMs, RAG and agentic architectures, MLOps and data platform governance, plus building and leading a high-performing Data & AI team and partnering with Product, Engineering, Architecture and Security to scale prototypes into production.

Helsing · Berlin, Germany

Builds and benchmarks computer vision systems for scene matching, geo-registration, SLAM, visual odometry and 3D reconstruction, adapting state-of-the-art geometric deep learning to real-world conditions and constrained hardware. Works within a scene understanding and localisation team, collaborating across research, engineering and product to integrate outputs into navigation and decision-making pipelines.

Scale AI · United Kingdom

Fellows design domain-specific problems and datasets to evaluate generative AI models, then provide expert insight to improve their accuracy and reasoning. Work is project-based with partnered AI labs, including co-authoring technical reports and research papers with Scale's research team. Fully remote, flexible 10-40 hour weeks within an interdisciplinary network.

IQVIA · Reading

Supports a global pharmaceutical client by applying statistical methods to biological, biomedical and omics datasets, building reproducible analytical workflows in R and/or Python. The role sits at the intersection of statistics, data science and AI, developing AI-enabled tools, agents and automation that speed up discovery and translational research decisions alongside scientists and engineers.

Isomorphic Labs · London

Lead and contribute to core machine learning research applying large language models to drug discovery, developing and refining LLM-driven approaches across biology and chemistry problems. Work spans post-training, reasoning, test-time scaling, tool-use, alignment, agents, reinforcement learning and fine-tuning, with model training and inference engineering at scale. Interdisciplinary collaboration with research scientists and engineers, plus technical mentorship and project leadership depending on experience.