Snigdha Purohit
About Snigdha Purohit
Snigdha Purohit is a Machine Learning Engineer currently working at Motional in Milpitas, California. She has a background in electrical engineering and extensive experience in machine learning, having previously held positions at AppZen, Nylas, and Carnegie Mellon University.
Work at Motional
Snigdha Purohit has been employed at Motional as a Machine Learning Engineer since 2022. In this role, she focuses on engineering frameworks for autonomous driving technology. She has developed AI models aimed at enhancing object detection accuracy specifically for autonomous driving vehicles. This position is based in Milpitas, California, where she has contributed to advancing the company's capabilities in the autonomous vehicle sector.
Education and Expertise
Snigdha Purohit holds a Master of Science (M.S.) in Electrical and Computer Engineering from Carnegie Mellon University, where she studied from 2017 to 2018. She also earned a Bachelor of Technology (B.Tech.) in Electrical Engineering from Nirma University, Ahmedabad, Gujarat, India, from 2012 to 2016. Her educational background provides a strong foundation in machine learning and data science, complemented by extensive programming experience in Python, C++, and C.
Background
Snigdha Purohit has a diverse background in machine learning and data science, with experience in various roles across multiple organizations. She began her career with internships at institutions like the Indian Institute of Technology, Gandhinagar, and Near Earth Autonomy. She later worked as a Research Assistant at Carnegie Mellon University and held positions at AppZen and Nylas, where she developed her skills in data mining and machine learning algorithms.
Achievements
Throughout her career, Snigdha Purohit has made significant contributions to the field of machine learning. She has implemented deep learning algorithms such as CNN, RNN, and LSTM using Tensorflow and PyTorch. Additionally, she has developed computer vision algorithms for agricultural applications and gained experience with IaaS platforms like AWS and Microsoft Azure for deploying machine learning models. Her work has focused on advanced techniques such as RCNN, Faster RCNN, ResNets, and GANs.