Rajas Bansal
Machine Learning Engineer at Together AI
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolRajas Bansal is a Machine Learning Engineer with nine years of experience, currently pursuing an MS in Computer Science at Stanford with an AI specialization and based in the San Francisco Bay Area. He co-founded Refuel as its founding ML engineer and joined Together AI after acquisition, bringing startup rigor to production ML systems. His work spans graph neural networks and recommender systems—published in KDD—and includes scalable GNN training at Amazon that preserved 98% accuracy using only 5% of nodes. Rajas combines research-tested ideas with production engineering from roles at Cohesity and research stints at Stanford and NUS, and has hands-on experience building low-level microarchitectures and high-scale ML pipelines. He looks for opportunities as a Machine Learning Engineer or Applied Scientist and enjoys projects that bridge cutting-edge research with deployable systems. Unusually, he pairs deep systems-level experience (Chisel/RISC-V OoO work) with modern ML at scale, enabling practical, performance-minded solutions.
9 years of coding experience
4 years of employment as a software developer
Indian Institute of Technology Delhi (IIT Delhi)High School, High School at Sanskriti SchoolMaster of Science - MS Computer Science, Master of Science - MS Computer Science at Stanford University
Stackoverflow
21reputation
509reached
2answers
0questions
Github Skills (71)
python10
data-science10
langchain10
machine-learning10
large-language-models10
llm10
huggingface-transformers10
openai10
parallelization9
deep-learning7
pytorch7
robot7
planning7
plutus6
crypto6
Programming languages (5)
Github contributions (5)
refuel-ai/autolabel
Jun 2023 - Feb 2025
Label, clean and enrich text datasets with LLMs.
Contributions:1 release, 186 reviews, 153 PRs in 1 year 8 months
anthropic-claudedata-sciencegpt-4huggingface-transformerslangchain
reail-iitd/commonsense-task-planning
Apr 2020 - Jun 2020
[RSS '20] Code repository for learning common sense knowledge for robot task planning. Contains simulation environment, data sets and models for learning tool use for a robot platform.
Contributions:23 commits in 1 month
roboticsdata-setsrsstask-planningknowledge
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