Design and build full-stack applications that integrate AI-driven physics simulation models into real-world engineering workflows. Collaborate directly with customers and technical teams to prototype, deploy, and iterate on high-impact tools across aerospace, energy, and advanced manufacturing. Work across backend services, APIs, and cloud infrastructure while contributing to core platform development.
Conduct foundational research to develop AI-driven physical simulation models for engineering and manufacturing applications. Translate complex physics and engineering problems into mathematical formulations, build and iterate machine learning models—particularly in neural operators, geometric deep learning, or generative modeling—and collaborate closely with engineers and customers to deliver real-world impact across advanced industries.
Design and optimize compiler software for NVIDIA's Grace CPUs, focusing on performance improvements for AI and HPC workloads. Collaborate with upstream open-source communities and contribute to next-generation CPU micro-architecture development. Work within a globally distributed engineering team to enhance the GNU Toolchain and LLVM-based infrastructure.
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.
This role involves designing and building infrastructure tooling to support scalable, secure, and highly available deployment processes for a modern core banking platform. The engineer will develop automation, observability, and orchestration systems, working closely with data and streaming platforms in cloud environments. A key focus is enabling reliable operations for both internal engineers and bank SREs through robust tooling and CI/CD integration.
A Senior Full Stack Engineer will design and implement end-to-end features across both frontend and backend systems, translating customer requirements into scalable, secure, and high-performance solutions. The role involves full ownership of features from design to production, with a strong emphasis on testing, collaboration, and delivering exceptional user experiences for global financial clients. Working within a cross-functional team, the engineer will help shape modern core banking technology on a cloud-native platform.
This role involves designing and implementing technical evaluation frameworks for AI systems, with a focus on model performance, safety, and responsible AI. The engineer will develop automated testing pipelines, translate policy and risk considerations into measurable benchmarks, and report findings to stakeholders. Work takes place in a government context using cloud platforms and agile practices, with hybrid attendance in London, Bristol, or Manchester.
Lead end-to-end technical delivery of custom AI agent solutions for enterprise clients in regulated industries, managing product scoping, stakeholder engagement, and deployment execution. Translate business objectives into technical roadmaps, coordinate cross-functional teams, and define measurable impact while navigating compliance and organizational complexity. Help build scalable deployment playbooks and feed real-world insights back into product and research.
Solution Architect - Data & AI Specialist - Northwest London - Hybrid 2/3 days per week on-siteSalary: £75,000 - £85,000 + Excellent BenefitsAs a Solution Architect, you'll play a pivotal role in defining and delivering technology strategies and roadmaps across...
Leads technical AI projects involving high-throughput vision systems on NVIDIA Jetson hardware, with a focus on real-time tracking, multi-modal sensor integration, and AI assurance. Provides technical leadership across the design lifecycle and mentors junior engineers. Works in multidisciplinary teams and engages with stakeholders to shape project direction and innovation in edge AI systems.
Design and govern software architecture and CI/CD pipelines for safety-critical autonomous and robotic systems, integrating control, computer vision, sensor fusion, and simulation. Work closely with R&D and military end users during field trials. Balance rapid prototyping with scalable, compliance-ready architecture.
Develop AI-driven video processing solutions within a deep tech environment, focusing on scalable media delivery and real-time systems. Work with Python and modern cloud infrastructure, integrating AI and video technologies to enhance content delivery. Collaborate on building maintainable, tested software for immersive media platforms.
This role involves collecting, analysing, and synthesising medical insights from diverse internal and external data sources to support strategic decision-making within a global pharmaceutical organisation. The analyst will contribute to innovative initiatives using AI-driven tools, including Large Language Models and AI-enabled analytics, to enhance insights generation. The position supports Medical Affairs teams in identifying trends and implications, working collaboratively in a multidisciplinary environment with a focus on data quality and strategic impact.
Design and build scalable, fault-tolerant systems for serving large language models (LLMs) in both research and production environments. Develop internal platforms for LLM capability discovery and integration, with a focus on performance, observability, and system health. Collaborate closely with researchers and engineers to optimize model deployment and support cutting-edge AI applications.
The role involves deep expertise in a specific reliability domain such as observability, performance engineering, or security-focused SRE, with responsibility for defining standards, building automation, and improving system resilience across a cloud-native platform. The engineer will lead incident response, develop tooling for scalability, and integrate reliability practices across Platform Engineering and DevSecOps. Emphasis is placed on cross-team collaboration, operational excellence, and driving long-term fixes for complex systemic issues.
