Develop automated tools for large-scale data analysis, vulnerability research, and exploit development. Investigate 0-day and n-day vulnerabilities by analyzing patches, malware, and system logs. Collaborate across teams to support threat intelligence and publish findings in blogs or conferences.
Conducts advanced vulnerability research and exploit development, analyzing patches and malicious artifacts to identify 0-day and n-day vulnerabilities. Develops automated tools for large-scale data analysis and collaborates across teams to publish findings and support threat intelligence efforts. Focuses on reverse engineering, proof-of-concept creation, and rapid response to emerging threats.
Act as a trusted technical advisor to academic and research institutions, driving adoption of NVIDIA's accelerated computing and AI technologies. Collaborate with researchers to integrate NVIDIA frameworks into large-scale projects, deliver training and workshops, and translate research needs into prototypical solutions. Focus on multimodal and physical AI, with deep engagement in GPU-accelerated computing and joint research initiatives.
Design and build full-stack applications that integrate AI-driven physics simulation models into real-world engineering workflows. Collaborate with data scientists and customers to develop, prototype, and deploy production-grade tools using Python, FastAPI, and cloud infrastructure. Work across industries such as aerospace, energy, and semiconductors, with opportunities for international travel to support on-site customer deployments and iterate on solutions based on direct user feedback.
Develop and scale AI-driven physical foundation models for engineering simulation, bridging research and production. Translate scientific prototypes into robust, distributed ML systems using high-performance computing and cloud infrastructure. Collaborate closely with researchers, engineers, and customers to solve real-world physics and engineering challenges.
This role involves developing and automating test frameworks for NVIDIA's large-scale distributed systems, with a strong focus on QA innovation using AI and LLMs. The engineer will design test plans, automate validation across UIs and APIs, integrate testing into CI/CD pipelines, and enhance testing efficiency using AI-driven tools. A key differentiator is the application of LLMs for test generation, prompt engineering, and QA workflow optimization in a high-performance computing environment.
Develop an engineering-grade spiking neural network (SNN) engine for real-world machine vision applications in high-stakes environments. Translate advanced research into robust, production-ready C++ software, integrating SNNs, CNNs, and Transformers. Work closely with researchers and engineers on synthetic data generation, image analysis, and deployment in defence and security contexts.
This role involves building and maintaining MLOps infrastructure to productionise advanced AI and computer vision models, from distributed training to deployment on cloud and edge devices. The engineer will optimise inference, manage model registries, and develop CI/CD and monitoring pipelines while working closely with research and software teams. The position requires strong hands-on skills in Python, containerisation, and GPU-accelerated systems, particularly NVIDIA Jetson platforms.
This role involves designing and building secure platforms to protect enterprise Generative AI and Large Language Model (LLM) applications from emerging threats such as prompt injection and adversarial inputs. The engineer will develop automated security testing frameworks, create robust APIs, and contribute to cloud-native, distributed systems operating at scale. Collaboration with global engineering, cybersecurity, and operations teams is central, with an emphasis on shaping secure AI adoption through resilient software design and best practices in AI security and DevSecOps.
This role involves supporting the design and development of AI solutions within a telecommunications environment, with a focus on AI integration, network optimisation, and cloud-based data projects. You'll work alongside experienced technologists, contributing to AI system testing, monitoring, and documentation while adhering to ethical AI practices. The position offers hands-on experience in real-world AI applications with structured training and mentorship.
This role involves conducting original research in AI safety, focusing on interpretability and behavioral analysis of transformer-based large language models. You'll design and run experiments, develop reproducible research tooling in Python with PyTorch, and translate technical findings for government, frontier labs, and national security partners. The position emphasizes scientific rigor, collaboration with senior researchers, and contributing to emerging AI safety standards.
This role involves designing and deploying AI agents capable of automating penetration testing tasks such as planning, tool use, and vulnerability identification. The engineer will build sandboxed cyber-lab environments using containers and Kubernetes, integrate security tools, and enhance agent performance through prompt engineering and cost optimisation. Collaboration with security researchers is key to translating offensive techniques into agent behaviours, within a scalable, polyglot production system.
This role involves developing a novel surgical robotics system as part of a multidisciplinary engineering team. The computer scientist will work on complex, ground-up projects requiring strong programming skills in C++, Python, and JavaScript, with close collaboration between electronics and mechanical engineers. Experience in image processing, computer vision, or robotics is advantageous, and a first-class degree from a top UK university is expected.
This role involves working on advanced surgical robotics systems within a multidisciplinary engineering team. The computer scientist will develop complex software solutions from the ground up, collaborating closely with electronics and mechanical engineers. The position requires strong programming skills in C++, Python, and JavaScript, with a focus on mathematically rigorous problem-solving in a research-driven medical technology environment.
This role involves designing and building scalable AI data pipelines and cloud-native microservices, with a focus on productionising machine learning, NLP, and generative AI models. The engineer will collaborate closely with data scientists and software engineers to transform research into robust, cloud-based systems using modern MLOps practices. Emphasis is placed on technical leadership, mentoring, and shaping engineering standards within a global AI team.
