Subhojeet Pramanik
Researcher at Softmax
Edmonton, Alberta, Canada
Summary
👤
Senior🎓
Top SchoolSubhojeet Pramanik is a researcher and machine learning engineer with a decade of experience specializing in reinforcement learning, scalable AI alignment, and applied ML systems. He has led RL and LLM-focused projects across industry and academia—driving productionized solutions at IBM, mentoring residents at Amii, and developing RL-based coding agents and RLHF methods at startups and research labs. His academic work produced a recurrent alternative to transformer self-attention optimized for long-context, partially observable RL tasks, demonstrating both computational efficiency and superior performance. Based in Edmonton, he blends hands-on engineering (ONNX/fastAPI deployments, vision transformers for high-res segmentation) with theoretical research into mathematical frameworks for organic human–AI alignment. Notably, he has repeatedly moved ideas from prototype to deployment, whether in elevator predictive maintenance, cloud integration features, or active noise cancellation using real-time RL.
10 years of coding experience
4 years of employment as a software developer
Master of Science - MS, Computer Science (thesis based), 3.9/4, Master of Science - MS, Computer Science (thesis based), 3.9/4 at University of AlbertaBachelor of Technology - BTech, Computer Science and Engineering, 8.88/10, Bachelor of Technology - BTech, Computer Science and Engineering, 8.88/10 at Vellore Institute of TechnologyHigh School, Science, 92%, High School, Science, 92% at Delhi Public School, Ruby Park
English, Hindi, Bengali
Github Skills (99)
python10
json-schema10
deep-learning10
async10
object-pooling10
api10
rest10
nlp10
json10
natural-language-processing10
pytorch10
machine-learning10
multi-task-learning10
twine10
architecture9
Programming languages (3)
Github contributions (5)
subho406/portfolio
Sep 2020 - Mar 2024
Contributions:161 pushes, 7 branches in 3 years 6 months
subho406/OmniNet
Jul 2019 - Nov 2020
Official Pytorch implementation of "OmniNet: A unified architecture for multi-modal multi-task learning" | Authors: Subhojeet Pramanik, Priyanka Agrawal, Aman Hussain
Contributions:1 review, 15 commits, 4 PRs in 1 year 3 months
pytorchmulti-modalmulti-taskmodaldeep-learning
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