Tom Pierce is an engineering manager with over a decade of hands-on experience building scalable web, video and big-data systems and a recent focus on ML engineering. He has led teams through cloud migrations and microservices adoption, shipping production APIs and analytics pipelines that handle datasets in the hundreds of billions of records. Comfortable coding across Python, TypeScript, Ruby and Java, Tom pairs practical engineering with people leadership—having line-managed teams of 2–8 and shaped hiring and onboarding processes. His open-source contributions span high-profile ML and RL projects (pytorch, numpyro) where he improved probabilistic tooling and distribution support, reflecting a strong grounding in statistical modelling. Based in Chesham, UK, he brings a track record of reducing operational overhead (CI rebuilds, platform automation) while delivering customer-facing features. Immediately available, he combines product-focused delivery habits with a curiosity for research-grade ML tooling.
10 years of coding experience
13 years of employment as a software developer
BEng Digital Media Engineering, BEng Digital Media Engineering at University of Surrey
Contributions:3 releases, 122 reviews, 1746 commits in 4 years 4 months
Contributions summary:Tom's primary contribution involved implementing new UI components for a Dash application using React and likely related libraries. They integrated Alert, Badge, Button, and ButtonGroup components, showing a focus on building interactive elements. Additionally, they added the styling for these components, making the application more visually appealing and likely integrating them with the chosen design framework, as well as refactoring existing components and updating the UI to create a cohesive user experience.
TensorDict is a pytorch dedicated tensor container.
Role in this project:
Back-end Developer
Contributions:218 reviews, 147 commits, 83 PRs in 4 months
Contributions summary:Tom contributed to the development of the tensordict library, specifically focusing on implementing a truncated normal distribution. They introduced a new TruncatedNormal class and associated utility functions within the existing distribution framework. The user also made changes to the continuous distributions and probabilistic modules by removing references to TorchRL. The user's work appears to be improving the statistical capabilities of the library.
pytorchdeep-learningdedicateddockertensor
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