Siddhartha Kamalakara
Founder at Ambition
Old Toronto, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolSiddhartha Kamalakara is a founder and machine learning engineer with nine years of experience building production-grade generative models and training frameworks, having co-written and co-led teams at Cohere on Fax (training framework), fine-tuning, and pretraining. He bridges industry and research—moving from ML-driven peptide design in biotech to staff engineering roles at Runway and a brief research stint at PsiQuantum—now returning to individual-contributor work on next-generation generative architectures. As an open-source-minded engineer, he contributed activation layers (SELU) and robust tests to the tiny-dnn C++ deep-learning framework, demonstrating low-level systems fluency beyond high-level model work. He teaches foundation modeling through the Toronto School Of Foundation Modelling and recently founded a stealth startup, reflecting a mix of mentorship, product focus, and technical entrepreneurship. Based in Old Toronto, he combines hands-on implementation, research rigor, and a specialty interest in peptide design that informs his approach to biologically inspired ML.
10 years of coding experience
8 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Manipal Academy of Higher Education
English, Hindi, Telugu
Stackoverflow
89reputation
1kreached
0answers
3questions
Github Skills (16)
neural-network10
machine-learning10
deeplearning-ai10
data-serialization10
c-language10
deep-learning10
cprogramming-language10
serialization10
unit-testing9
libpng6
floating-point6
tensor6
tensorflow6
python6
numpy6
Programming languages (5)
Github contributions (5)
tiny-dnn/tiny-dnn
May 2017 - Nov 2017
header only, dependency-free deep learning framework in C++14
Role in this project:
ML Engineer
ML EngineerContributions:10 commits, 12 PRs, 21 comments in 6 months
Contributions summary:Siddhartha primarily contributed to the implementation and testing of a new activation layer, Scaled Exponential Linear Units (SELU), for the tiny-dnn deep learning framework. They added the layer, serialization functions, and unit tests, ensuring its proper integration. Further contributions involved parameterizing and updating other activation layers (elu), as well as improving the formatting and conventions of the code.
deep-learningdependency-freec-plus-plusmachine-learningneural-network
srk97/optimus
Dec 2017 - Apr 2019
Contributions:13 commits, 10 pushes, 2 branches in 1 year 4 months
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