Gaurab Banerjee is a Machine Learning Engineer at Apple with nine years of hands-on experience building multimodal foundation models and sensor/vision systems, currently focused on post-training methods for audio-centric models. He has a strong applied research background—publishing Stanford papers on audio and generalized transformer pretraining and contributing to diffusion model SFT/RLHF work at Apple. His industry R&D includes sensor fusion and pose estimation patents from a Ford internship and full-stack vision/perception modeling for tracking and detection. Comfortable bridging research and engineering, he emphasizes rigorous evaluations and benchmarks to drive system understanding and product impact. Outside core ML work, he has deep teaching and course-development experience at Stanford, reflecting strong communication skills that help translate complex ideas into practical solutions.
9 years of coding experience
1 year of employment as a software developer
Advanced Regents Scientific Diploma with High Honors, Advanced Regents Scientific Diploma with High Honors at Shaker High School
Master of Science - MS, Computer Science, 4.15, Master of Science - MS, Computer Science, 4.15 at Stanford University
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Gaurab Banerjee - Machine Learning Engineer at Apple