Daniel Whettam is a Lead Machine Learning Engineer based in Bristol with eight years’ experience applying state-of-the-art computer vision and ML to geophysical and audio-visual problems. He holds a PhD in Interactive AI/Computer Vision and has moved research into production—building an internal ML framework that handles training, evaluation and deployment at Sulmara. Previously as sole ML developer at Beam he automated seabed prediction to match expert performance while delivering a >4x speedup and ~£400k savings, and helped mature MLOps and code-quality practices. His academic work spans self-supervised, multi-modal learning and industry stints include novel view synthesis on GPUs (NeRF/Gaussian Splatting) and transfer-learning for low-resource speech dialects. Comfortable across research, engineering and MLOps, he combines deep technical rigor with a proven track record of turning complex models into operational systems. Colleagues would note his knack for building pragmatic infrastructure from scratch that accelerates both experimentation and deployment.
8 years of coding experience
1 year of employment as a software developer
Master of Science - MS, Data Science, Merit, Master of Science - MS, Data Science, Merit at The University of Edinburgh
Bachelor of Science - BS, Computer Science, First Class Honours, Bachelor of Science - BS, Computer Science, First Class Honours at The University of Hull
Doctor of Philosophy - PhD, Interactive Aritifical Intelligence, Passed, Doctor of Philosophy - PhD, Interactive Aritifical Intelligence, Passed at University of Bristol
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Daniel Whettam - Lead Machine Learning Engineer at Sulmara