Lead the development of next-generation AI systems for cybersecurity, focusing on large language models and deep learning at scale. Design and implement production-ready AI solutions that process trillions of events daily. Provide technical leadership, mentor research teams, and shape AI strategy while driving innovation in reasoning, summarisation, and interactive systems.
Lead and grow a team of engineers building foundational infrastructure-as-code platforms at a high-scale cybersecurity company. Focus on cloud cost optimization, multi-cloud deployment standards, and enabling engineering velocity through reusable, self-service infrastructure. Work with large-scale distributed systems processing trillions of events daily, leveraging AI and modern DevOps practices to ensure reliability and performance.
This role involves working closely with government clients and partners to design and deploy AI solutions using NVIDIA's hardware and software technologies. The Solution Architect will develop technical strategies, create educational resources, support proofs of concept, and guide field deployments in areas like computer vision, generative AI, and digital twins. Emphasis is placed on translating public-sector challenges into scalable, AI-driven outcomes while advocating for product improvements based on real-world feedback.
This role involves working closely with government clients and internal teams to design and deploy AI-driven solutions that address public sector challenges. The Solution Architect will develop technical strategies, create educational resources, lead proof-of-concept initiatives, and support the implementation of AI workflows using NVIDIA's technologies. A strong focus is placed on computer vision, generative AI, and deep learning deployment in real-world government environments.
Design and build full-stack applications that integrate AI-driven physics simulation models into real-world engineering workflows. Collaborate with customers on-site and remotely to develop, prototype, and iterate tools that solve complex engineering challenges across aerospace, energy, and manufacturing. Work across backend services, APIs, and cloud infrastructure while contributing to core platform development and shaping product direction based on cross-industry use cases.
Develops high-fidelity electromagnetic simulations across low- and high-frequency domains, focusing on semiconductor, aerospace, and automotive applications. Builds and validates complex EM models, automates simulation workflows, and collaborates with data science teams to train AI surrogates. Works on-site with customers to solve engineering challenges using advanced simulation and AI-driven tools.
This role involves developing and training advanced machine learning models for digital identity verification, with a focus on areas like deepfake detection, bias mitigation, document understanding, and efficient on-device ML. The candidate will conduct research, build multimodal models using synthetic data, optimise training performance across GPUs, and collaborate with engineering teams to deploy solutions globally. Work includes creating large-scale datasets, benchmarking vision-language models, and improving fairness in biometric systems.
Develop advanced mathematical and machine learning models for complex industrial and scientific systems by combining first-principles physics with AI techniques. Work across the full modelling lifecycle using large-scale sensor and operational data to build predictive digital representations of physical processes. Collaborate with a high-calibre team to improve model performance through experimental design and real-world validation.
This role involves designing, building, and deploying advanced machine learning models to solve complex operational challenges in real-world environments. You will work closely with a high-calibre founding team, industrial data, and customer environments to take machine learning systems from early validation through to scalable deployment.
This role involves designing, training, and deploying computer vision models, working across the full machine learning lifecycle. You will build production-ready systems, evaluate model performance, and contribute to technical decisions across various projects, collaborating closely with engineers, data scientists, and domain experts.
Develop and scale generative and predictive machine learning models for cellular behaviour using multi-omic biological data. Build robust distributed training and inference systems, bridging ML research and production deployment in close collaboration with scientists. Optimize performance across compute, memory, and I/O while advancing core ML infrastructure in a highly interdisciplinary environment.
Lead the technical direction and delivery of complex AI/ML projects for clients in highly regulated sectors, designing scalable systems and mentoring engineering teams. Drive innovation by building reusable solutions and guiding architectural decisions across full-stack AI deployments. Act as a technical coach for both internal teams and external stakeholders in ambiguous, high-risk environments.
Designs and governs software architecture and CI/CD pipelines for autonomous military systems, integrating robotics, computer vision, sensor fusion, and simulation platforms. Ensures scalable, maintainable, and safety-compliant software frameworks in a defence R&D environment, supporting real-world field trials with military users.
Design and govern software architecture and CI/CD pipelines for safety-critical autonomous and robotic systems, integrating control, computer vision, sensor fusion, and simulation platforms. Support system integration and field trials with military users while ensuring scalable compliance with defence standards. Work in a hybrid setup with flexible hours and early Friday finishes.
Work on a 28-month KTP project to develop an AI platform that extracts structured building data from UK planning drawings, improving risk assessment for insurers and property stakeholders. Operate at the intersection of AI, computer vision, and property data, with responsibilities spanning research, development, and commercialisation. Collaborate closely with both academic supervisors and an InsurTech scale-up, balancing technical experimentation with real-world application.
