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Abhijit Balaji has a diverse work experience in the field of machine learning and software engineering. Abhijit started their career as a Data Science Engineer at ENDOTHERM FLUIDS (INDIA) PRIVATE LIMITED, where they developed and deployed a CNN-based machine vision system for detecting surface defects in metal components. Abhijit also managed a team that engineered a robotic conveyor system for quality inspection, and subsequently launched a startup called JIDOKA Technologies, providing AI-based custom machine vision solutions.

After that, Abhijit worked as a Data Scientist at Straive, where they built an AI system for describing chart images and improved online accessibility for the visually impaired. Abhijit also designed and deployed a CNN-based tagging system for biomedical research papers, enhancing the search capability of the paper search engine. As a team leader, they developed an AI proof-reader that significantly improved the productivity of human copy editors.

Abhijit then joined Explosion as a Research Assistant, where they programmed a deep learning library called "Thinc" and enabled backpropagation and training compatibility across multiple frameworks. Abhijit also reduced the training RAM footprint of a neural co-reference resolution system.

Abhijit continued their research work as a Research Assistant at The University of Texas at Dallas, where they gained valuable experience before transitioning to Samsung Electronics America as a Machine Learning Research Engineer. In this role, they conducted research on computational photography and developed deep learning models for portrait image relighting.

Next, Abhijit worked at Adobe as a Machine Learning Engineer, focusing on fine-grained multi-modal representation learning. Abhijit built a text-to-image retrieval system for Adobe Stock and developed a training methodology that improved the performance of the CLIP model using Denotation Graphs.

Abhijit'smost recent position was at Google as a Software Engineer, where they worked on enabling perception and on-device machine learning at Nest. Abhijit created deep learning and generative models for real-time human tracking and gaze correction. Abhijit also engineered an on-device system for model updates in Nest doorbells and cameras.

Abhijit's work experience demonstrates their expertise in machine learning, deep learning, and software engineering, as well as their ability to contribute to various projects in different industries.

Abhijit Balaji has pursued a Master of Science (MS) degree in Computer Science from The University of Texas at Dallas. Prior to that, they completed a Bachelor of Technology (BTech) degree in Mechanical Engineering from Vellore Institute of Technology.