Claire Neveu
Lead Engineer at The Recount
New York, New York, United States
Summary
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RockstarClaire Neveu is a Lead Engineer based in New York with 13 years of experience building reliable full-stack systems and leading engineering efforts. She pairs hands-on development with practical leadership at The Recount, shipping features and maintaining production-grade services. An active open-source contributor, Claire has improved both web UI and backend APIs for Determined AI—helping streamline distributed ML workflows—and enhanced Scala linting and build reliability in wartremover. Her strengths include refactoring complex systems, tightening authentication and RBAC flows, and integrating UI workflows with backend access controls. Colleagues rely on her to translate ambiguous requirements into maintainable code and to quietly improve developer experience across stacks. She brings a pragmatic, security-aware mindset to scaling products and teams.
12 years of coding experience
Github Skills (19)
javascript10
apidoc10
macros10
typescript10
scala10
api10
typescript-types10
linter10
typescripts10
react10
testing9
rbac9
webui9
mlops8
pytorch8
Programming languages (17)
Github contributions (5)
wartremover/wartremover
May 2015 - Jul 2018
Flexible Scala code linting tool
Role in this project:
Back-end Developer
Back-end DeveloperContributions:13 releases, 111 commits, 85 PRs in 3 years 2 months
Contributions summary:Claire primarily contributed to the development of the wartremover tool, focusing on implementing and refining various code linting features. Their work included modifying the linting rules for specific Scala language constructs (e.g., Enumeration, null usage) and improving the overall functionality of the tool. They also refactored code to improve efficiency and added new warts, ensuring better code quality. The user has also demonstrated improvements in the build process with version changes and upgrades.
lintermacroslintingmetaprogrammingcompiler-plugin
determined-ai/determined
Oct 2022 - Dec 2022
Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:775 reviews, 8 commits, 46 PRs in 2 months
Contributions summary:Claire primarily worked on the web UI components and the backend API endpoints within the Determined AI platform. They were responsible for fixing bugs related to authentication and RBAC configurations, and for implementing a user edit modal integrating with RBAC endpoints. Furthermore, they refactored the agent context within the application and corrected experiment listing functionalities. Their contributions also involved modifying API configurations and integrating new features within existing workspaces.
pytorchdata-sciencedeep-learningdistributed-trainingmachine-learning
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