Max Smolin
Founding Engineer at Latch Bio
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolMax Smolin is a founding engineer with 11 years of experience building production-ready scientific software and distributed systems from the ground up, currently based in San Francisco. At Latch Bio he architected core products, grew revenue to $1M+ ARR, and led a team of seven while pushing rigorous typing, code review, and platform-level workarounds across AWS, Docker, React, and Postgres. His contributions span bioinformatics tooling, a custom distributed file system and on-demand compute environment, and a reactive Python runtime for data analysis—work that required self-study of biology to translate domain research into usable software. An active open-source contributor, he has improved build systems for legacy C++ projects and enhanced Ray’s ML examples and CI, showing a knack for platform interoperability and developer experience. He also evaluates and mentors engineering talent, having designed on-site projects and overseen junior engineers’ growth.
11 years of coding experience
1 year of employment as a software developer
Bachelor of Arts - BA Computer Science, Bachelor of Arts - BA Computer Science at University of California, Berkeley
English, Russian
Stackoverflow
33reputation
3kreached
0answers
2questions
Github Skills (24)
debugging10
pytorch10
debug10
c-language10
python10
machine-learning10
source-engine10
sdk10
ray10
cprogramming-language10
tensorflow9
deeplearning-ai9
deep-learning9
macos9
distribute8
Programming languages (12)
Github contributions (5)
ray-project/ray
Jan 2020 - Nov 2020
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
ML Engineer
ML EngineerContributions:20 commits, 22 PRs, 100 comments in 9 months
Contributions summary:Max primarily contributed to improving and maintaining examples and tests related to the RaySGD framework. Their work involved updating dependencies, refactoring code to make PyTorch dependency optional, and clarifying the behavior of TensorFlow training strategies. They also made improvements to the CI/CD pipeline and documentation. This suggests a focus on enhancing the usability and functionality of RaySGD for machine learning tasks.
pythonconsistsruntimetensorflowserving
alliedmodders/hl2sdk
Jan 2019 - Jan 2019
Half-Life 2 SDK Mirrors
Role in this project:Backend Developer
Backend DeveloperContributions:8 commits, 4 PRs, 3 comments in 1 day
Contributions summary:Max primarily focused on resolving build issues and fixing warnings related to platform-specific configurations for Mac debug builds within the Half-Life 2 SDK. Their contributions involved modifying header files to address compilation problems, including missing dependencies and conflicting definitions. The user also worked on addressing unused variables and compiler assertions to improve code quality and maintainability, indicating a focus on platform-specific build configurations and code correctness.
source-engine-sdkhalf-lifesdksource-enginemirrors
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