NVIDIA is seeking a Senior MLOps Engineer to join our DSX Enablement team, collaborating closely with strategic customers to implement and enhance groundbreaking AI workloads. We partner with the world's most innovative AI companies and open-source communities to address their...
Develop and optimize compilers for AI inference workloads on next-generation NVIDIA GPUs, focusing on kernel generation, computational graph optimizations, and hardware-software co-design. Collaborate with cross-functional teams to advance AI performance and scale across datacenters. Work at the forefront of compiler innovation for deep learning and AI architectures.
This role involves building and maintaining a secure, high-performance sandboxing platform for AI agent workflows, focusing on strong isolation, low-latency execution, and developer experience. The engineer will design APIs, optimise resource utilisation, debug production issues, and collaborate with internal teams to shape the platform’s evolution across containerised and virtualised environments. It requires deep systems expertise in Linux internals, virtualisation, and developer-facing tooling.
Design and build scalable, cloud-native systems and user interfaces that integrate LLM-powered capabilities into financial workflows. Work closely with AI and product teams to deliver production-ready agentic systems on AWS. Focus on event-driven microservices, React front-ends, and robust APIs using Node.js and TypeScript.
This role involves designing and maintaining large-scale, performance-sensitive distributed systems that serve Claude to millions of users globally. The engineer will work across the full inference stack, building intelligent routing, autoscaling, and deployment systems for AI models running on diverse accelerators across multiple cloud platforms. The position emphasizes real-time system resilience, compute efficiency, and close collaboration with research teams to enable next-generation model development.
Design and build reliable, multi-step agentic systems for high-stakes government applications, focusing on AI safety, red-teaming, and model optimization in regulated environments. Develop novel architectures and evaluation frameworks to ensure sovereign AI systems are performant, unbiased, and production-ready at scale.
Applied AI Engineer (C# .NET) - Nottingham - Hybrid (2 days a week in office) - £80,000We're working with an ambitious technology company developing intelligent software products used by thousands of customers across the UK and internationally. They're investing heavily...
This role involves designing and delivering AI-driven features for a SaaS platform, from concept through to production. You'll work across the full product lifecycle, integrating LLMs and AI agents into customer-facing products using modern cloud technologies and full-stack development. Collaboration with Product and Design teams is key, with a focus on rapid prototyping, intelligent workflows, and shipping end-to-end features used by real users.
This role involves building and maintaining AI evaluation frameworks to test non-deterministic outputs at scale. You'll develop automated tooling, versioned golden datasets, and work with LLM-as-judge scoring to ensure AI reliability across engineering squads. The position offers influence over quality practices in a truly AI-first environment with a focus on solving complex, real-world AI testing challenges.
Lead the design and deployment of scalable AI and generative AI solutions, including LLM-based applications for enterprise search, document summarization, and conversational AI. Drive innovation through retrieval-augmented generation (RAG) architectures and mentor engineering teams in transformer models and prompt engineering. Collaborate across data, ML, and business teams to deliver measurable impact using cloud AI platforms and MLOps practices.
Lead and scale a security engineering team at the forefront of AI-driven drug discovery, designing and implementing a robust, AI-first security architecture. Translate complex scientific and business objectives into secure, automated systems while enabling high-velocity research in a regulated, cloud-native environment. Collaborate closely with ML researchers, medical teams, and Google/Alphabet partners to secure cutting-edge digital biology platforms.
Lead the engineering team behind Multiverse's Diagnose & Prescribe (D&P) system, which assesses skill gaps and guides learners and employers to the right apprenticeships. You'll own technical direction, team health, and delivery across AI-driven services including RAG, LLM-scored assessments, eligibility logic, and onboarding flows. Work closely with Product, Design, and GTM to shape an AI-native platform while evolving services out of a legacy monolith.
This role involves researching and developing novel attack vectors against large language model safeguards to identify vulnerabilities before malicious actors can exploit them. You'll design automated attack systems, evaluate LLM robustness through methods like jailbreaking and data poisoning, and collaborate with leading AI labs to improve defenses. The work directly informs policy and safety practices at frontier AI companies and government levels.
This role involves designing and improving the ML platform systems that support model training, evaluation, and production serving at scale. The engineer will build infrastructure and internal tooling to enhance reliability, scalability, and automation, with a focus on reducing operational overhead for researchers and product teams. Work includes developing agentic workflows, optimizing GPU-based workloads, and improving observability and developer experience across distributed systems.
Build and ship end-to-end AI agent systems that transform how Multiverse operates, from context design and tool integration to evaluation and production operation. Work across the full stack to reinvent internal workflows and set the blueprint for AI-first companies. Use AI-assisted development with Claude Code and shape technical direction in a small, high-impact team.
