Alphonso Crawford
Senior Software Engineer at Discord
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAlphonso Crawford is a Senior Software Engineer based in San Francisco with 7 years of experience building resilient full-stack systems and production-facing developer tooling. He has a track record at companies like Unity, Meta, Roblox, and now Discord, delivering high-scale runtime services (including a Unity configuration service that served 1.2M DAU) and managing deployments, monitoring, and incident response. Skilled in object-oriented design, algorithms, testing, and API documentation, he bridges frontend dashboards and backend services—often owning end-to-end delivery from data model to cache and runtime evaluation. He also contributes to open-source ML tooling as an MLOps engineer on Unity-Technologies/ml-agents, improving environment handling and test coverage to make training workflows more reliable. Colleagues rely on him for clear technical communication and pragmatic refactors that reduce operational friction while maintaining robust code quality.
7 years of coding experience
11 years of employment as a software developer
Bachelor of Science (BS) Computer Software Engineering, Bachelor of Science (BS) Computer Software Engineering at Arizona State University
English
Github Skills (10)
mlops10
python10
unity3d9
machine-learning8
deep-reinforcement-learning8
dockers7
testing7
pytest7
docker7
tensorflow6
Programming languages (1)
Github contributions (2)
Unity-Technologies/ml-agents
Feb 2020 - Feb 2020
The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
Role in this project:
MLOps Engineer
MLOps EngineerContributions:18 commits, 2 PRs, 17 pushes in 11 days
Contributions summary:Alphonso primarily focused on improving the environment handling within the ML-Agents toolkit. Their contributions involved implementing environment launch checks, validating environment paths, and refactoring related code. They also worked on addressing pylint errors and adding a test case for a bad environment path, ensuring robustness and maintainability of the project. These changes seem geared towards simplifying environment configuration and improving the reliability of the training process.
servedeep-reinforcement-learningtrainingunityreinforcement
alphonsocrawford/alphonso_emacs
Oct 2018 - Oct 2018
Contributions:1 branch in 1 day
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