Daniel Corvesor is a Senior Machine Learning Engineer based in London with a strong foundation in mathematics (BSc) and an MSc in Machine Learning from UCL, and 16 years of hands-on experience across industry and open source. He builds full-stack ML applications for government clients at Faculty, and previously applied ML and research skills in healthcare robotics at Medtronic Digital Surgery and energy at Shell. Comfortable across backend systems, build tooling and CI, his prolific open-source contributions include improvements to Bazel, Cargo, Rust tooling and widely used projects like Selenium and Synapse—work that often focused on build reliability, cross-toolchain compatibility and clearer diagnostics. He has startup CTO experience, has taught coding to refugees and new developers, and blends product-minded engineering with rigorous reproducibility practices. Notably, his OSS patches span low-level build-system fixes to web framework session handling, revealing a rare mix of systems, automation and applied ML expertise.
16 years of coding experience
5 years of employment as a software developer
Bachelor of Science - BSc Mathematics, Bachelor of Science - BSc Mathematics at University of Bath
High School A-Levels and GCSEs, High School A-Levels and GCSEs at Harlington Upper School
A fast build system that encourages the creation of small, reusable modules over a variety of platforms and languages.
Role in this project:
Back-end Developer
Contributions:602 commits, 8 PRs, 2 pushes in 9 months
Contributions summary:Daniel's commits focused on implementing and improving build tool functionalities within the facebook/buck repository. They worked on resolving `SourcePath` issues and refining the Maven integration. They also made changes to the D compiler and related rules.
Contributions:8 releases, 105 reviews, 588 commits in 5 years 4 months
Contributions summary:Daniel primarily contributed to build system and process execution improvements. They fixed build-related issues, corrected code formatting, and migrated the project to use Rust 2018. They also worked on optimizing process execution, including adding functionality to the cache and fixing potential issues related to multi-platform execution. The user demonstrated skills in modifying build processes, working with Rust, and improving the performance of the project's build and testing pipelines.
pythonprotobufaws-lambdapantsshell
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Daniel Corvesor - Senior Machine Learning Engineer at Faculty