David Ross is a pragmatic software engineer with 13 years of experience building reliable systems across Rust, Go, Python, and embedded C. Currently an SDE II at Amazon in Seattle, he has shipped production services at Fastly (image optimization) and firmware for RISC-V devices at Western Digital, emphasizing thorough testing, single sources of truth, and safe programming. A prolific open-source contributor, he’s improved compilers, Rust crates (serde-enabled arrayvec, msgpack), and tooling for the Rust trait system (chalk), often adding robust tests and clearer error messages. He combines low-level embedded expertise with cloud-native deployment experience (GKE, Kubernetes, cgo), and routinely refactors for readability and maintainability. Notably, his work on Transcrypt and other cross-language tooling shows a knack for bridging languages and improving developer ergonomics.
13 years of coding experience
4 years of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at University of Washington
CloudBot - The simple, fast, expandable, open-source Python IRC Bot!
Role in this project:
Back-end Developer
Contributions:504 commits, 19 PRs, 51 pushes in 4 years 11 months
Contributions summary:David contributed to the CloudBot project by refactoring the core `cloudbot.py` file to utilize a 'CloudBotWrapper' object for improved structure. They also fixed conflicts and bugs in the correction plugin, and introduced changes to the `bot.py` file, by reorganizing the 'setup' function. Furthermore, the user added features to the admin module, which indicates a focus on server-side functionality. The user demonstrates proficiency in Python and knowledge of IRC bot development.
An implementation and definition of the Rust trait system using a PROLOG-like logic solver
Role in this project:
Back-end Developer
Contributions:2 reviews, 7 commits, 4 PRs in 2 years 4 months
Contributions summary:David contributed to the `chalk` repository, which implements the Rust trait system. Their primary focus was on improving the code's readability and maintainability, as seen in commits like cleaning up extraneous code and adding basic indentation. The user also made significant improvements to the rendering of various components, including `ImplDatum`, `ApplicationTy`, and `TraitDatum`, and introduced simplification tests for the codebase. These contributions indicate a strong understanding of the trait system and its implementation details.
traitrustsolverlogicdefinition
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David Ross - Software Development Engineer II at Amazon