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Rajas Bansal has a diverse work experience in various roles and companies. Rajas most recently worked as a Machine Learning Engineer at Refuel starting in 2023. Prior to that, they were an Applied Science Intern at Amazon from June 2022 to September 2022, where they developed scalable training methods for Graph Neural Networks.

Rajas also worked as a Research Assistant at Stanford University's SNAP lab from September 2021 to September 2022, focusing on recommender systems and graph neural networks. During this time, they worked on developing backward compatible embeddings for industry settings.

Before Stanford, Rajas was a Research Engineer at Cohesity from August 2020 to September 2021. Rajas also gained experience as an Undergraduate Teaching Assistant at the Indian Institute of Technology, Delhi, from July 2019 to May 2020, and as a Secretary at ACES-ACM, IIT Delhi, from July 2019 to May 2020.

Rajas has also had internship experiences, including a Summer Internship at Tower Research Capital in 2019 and a Research Internship at the National University of Singapore in 2018. In the latter role, they implemented a microarchitecture that improved performance and resource contention.

Additionally, Rajas served as a Journalist for the Board of Student Publications at IIT Delhi from May 2017 to May 2018. Overall, they have a well-rounded background with experience in machine learning, research, teaching, and journalism.

Rajas Bansal's education history can be summarized as follows:

From 2021 to 2023, Rajas Bansal pursued a Master of Science (MS) degree in Computer Science at Stanford University.

Between 2016 and 2020, Rajas Bansal attended the Indian Institute of Technology, Delhi, where they earned a Bachelor of Technology degree in Computer Science.

From 2001 to 2016, Rajas Bansal studied at Sanskriti School, completing their high school education. No specific field of study was mentioned for this period.