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Director of AI and Physical AI Segment @ NVIDIA
Applied scientist by background, with close to twenty years in AI and machine learning. For the last decade I have been at NVIDIA, working at the intersection of research and production — both hands-on and as the leader of a ~30-person organisation that turns frontier research into production systems at scale. NVIDIA has been a unique vantage point — a front-row seat to the AI revolution and the infrastructure that made it possible. On the research side, I focus on technologies that enable the scalability of AI systems. That has meant contributing to the design and bring-up of some of the world's first AI systems (DGX-1 and DGX Station — still have a prototype in my home office) and AI supercomputers like the UK JADE system. It has also meant working on some of the earliest FP16, BF16, FP8 and now NVFP4 training regimes, on algorithms like LARC and tools like Horovod and Megatron-LM to enable large-batch training, and today on the challenges of efficient MoE training, distributed RL and Physical AI. Throughout, I have worked directly with networking and storage engineering teams — translating AI requirements into hardware and software design and contributing to NVIDIA's reference architectures. The organisation I have built covers distributed training, training resiliency, model compression, inference and — more recently — Robotics, Physical AI and world models. The team carries extensive research pedigree: contributions to reduced precision training, GPTQ, zero-bubble pipeline parallelism, Nemotron, world models (Cosmos) and AI in structural biology. We collaborate through joint applied research with organisations across the full European AI landscape: AI-native companies, frontier research labs, academic institutions and large-scale HPC centres. Beyond the technical work, I have played a broader role at NVIDIA — helping establish the Deep Learning I
Coventry University
Doctor of Philosophy (Ph.D.), Information Retrieval
January 1, 2008 – January 1, 2012
Coventry University
Bachelor's Degree, Information Retrieval
January 1, 2006 – January 1, 2007
The Silesian University of Technology
Computer Science
January 1, 2002 – January 1, 2009
NVIDIA
Director of AI and Physical AI Segment
January 1, 2017 – Present
United Kingdom · Remote
Capgemini
Senior Data Scientist
January 1, 2016 – December 1, 2016
Jaguar Land Rover
Research Strategy Engineer - Self Learning Car
February 1, 2012 – January 1, 2016
International Digital Laboratory, Warwick University
Coventry University
Researcher / PhD student
January 1, 2008 – January 1, 2013
Greater Coventry Area
Coventry University
Part Time Lecturer / Researcher
September 1, 2007 – January 1, 2012
Greater Coventry Area
Trinity Expert Systems
Software Developer .NET
August 1, 2007 – January 1, 2012
Greater Coventry Area
Microsoft
Software Design Engineer in Test
June 1, 2006 – October 1, 2006
Warsaw
Cultural Fit Analysis
The candidate's career trajectory, moving from academic research to industry leadership roles at major technology companies (Microsoft, Jaguar Land Rover, Capgemini, NVIDIA), demonstrates adaptability and a drive for impact. Their involvement in diverse projects, from self-learning cars to public sector data platforms and cutting-edge AI research, indicates a broad interest and ability to contribute across various domains. However, the target role of 'Data Analyst' appears to be a significant mismatch with their senior-level AI/ML leadership experience, which could lead to underutilization of their advanced skills and potential dissatisfaction.
Soft Skills & Operational Fit
The candidate's extensive experience in leadership, strategic planning, and managing external partnerships suggests strong communication, collaboration, and problem-solving skills. Their work at NVIDIA and Jaguar Land Rover indicates an ability to operate effectively in high-stakes, research-intensive environments. The description of defining research agendas and representing NVIDIA on industry forums points to strong strategic thinking and influence.