Maksim Kouznetsov
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMaksim Kouznetsov is a Senior Engineering Manager based in San Francisco with 25+ years of software engineering experience and a decade of formal leadership in AI and ML platforms. He recently joined AlphaSense to lead Reference and Entity Data Systems after building generative AI and deep learning platforms at C3.ai and Hewlett Packard Enterprise, where he shipped a Generative AI Studio and a RAG-focused low-code ML framework. Hands-on by background, Maksim has contributed code to the Determined AI open-source training platform—improving experiment management and Apple Silicon support—reflecting his continued engagement with ML infrastructure. He combines deep academic training in applied mathematics and operations research with a track record of forming and scaling distributed teams and shipping production-grade platforms under tight constraints.
10 years of coding experience
16 years of employment as a software developer
DEA (MS), Computer Science and Operations' Research, DEA (MS), Computer Science and Operations' Research at Pierre and Marie Curie UniversityLomonosov Moscow State University
French, Russian
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (14)
pytorch10
machine-learning10
distributed-training10
deep-learning10
python10
data-science9
experiment9
mlops8
debug8
tensorflow8
testing7
hyperparameter-tuning6
kubernetes6
kubernetes-pods6
Programming languages (6)
Github contributions (5)
determined-ai/determined
Dec 2021 - Jan 2023
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:
ML Engineer
ML EngineerContributions:308 reviews, 54 commits, 160 PRs in 1 year 1 month
Contributions summary:Maksim primarily contributed to the Determined AI platform by implementing new features and addressing bugs related to experiment management and trial handling. Their work includes adding Python methods for listing trials within experiments, as well as fixing issues related to experiment performance. They also worked on supporting Apple Silicon and addressing pickling issues in test environments. The user demonstrated skills in modifying the codebase and related testing to improve the platform.
pytorchdata-sciencedeep-learningdistributed-trainingmachine-learning
mpkouznetsov/determined
Nov 2021 - Apr 2023
Determined: Deep Learning Training Platform
Contributions:304 pushes, 124 branches in 1 year 5 months
pytorchcaffe2deep-learningmachine-learningtraining
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