Nitin Jain
@nitinjain
I'm a machine learning engineer building efficient on-device models for EMG, handwriting, and conversational AI.
United StatesWhat I'm looking for
At Meta, I lead the end-to-end ML lifecycle from EMG research through production inference for the MetaNeuralBand device. I've built training recipes, standardized evaluation suites, and hardware-emulator-in-the-loop benchmarking pipelines.
I develop on-device quantization, sparsity, compression, and power optimization for EMG and handwriting models, including sub-8-bit sparse models under strict power and latency budgets. I've also contributed mixed-precision inference optimizations for Attention and RNN modules to PyTorch and ExecuTorch, and collaborated with ARM on accelerator delegation.
Previously at Amazon Alexa, I led a proactive ML-driven latent goal-solving service scaling to 200+ TPS and launched contextual understanding for multi-turn NLU interactions. My research includes NLP event extraction, kernel learning, matrix computation, and numerical methods.
Experience
Work history, roles, and key accomplishments
Led the end-to-end ML lifecycle from EMG research to production model inference, culminating in the launch of the MetaNeuralBand device. Spearheaded research and production of on-device quantization, sparsity, compression, and power optimization for EMG and handwriting models.
Led the development of a proactive, ML-driven latent goal-solving service scaling to 200+ TPS, doubling system throughput through lazy caching and rule engine optimizations. Launched an industry-first contextual understanding solution under Alexa's NLU service for multi-turn interactions.
Researched kernel learning algorithms over convex spaces and designed memory-efficient structures to extract a 150GB DNS census bipartite graph.
Developed a hierarchical model to extract sports events and tag entities from Twitter's streaming API, curating a dataset of 1,000+ classified tweets.
Created a patentable Face detection based Auto-Focus algorithm with Non-linear regression Bayesian.
Undergraduate Researcher
R.V College of Engineering
Jun 2012 - May 2013 (11 months)
Researched fast numerical methods to compute the n root of a number.
Education
Degrees, certifications, and relevant coursework
Purdue University
Master of Science, Computer Science
2022 -
Pursuing a Master of Science in Computer Science with a focus on Machine Learning, NLP, and Optimization theory.
Purdue University
Bachelor of Science, Computer Engineering
Grade: 3.74
Graduated with a Bachelor of Science in Computer Engineering, achieving a cumulative GPA of 3.74.
R.V. College of Engineering
Bachelor of Engineering, Computer Engineering
Grade: 9.15/10
Earned a Bachelor of Engineering in Computer Engineering with a concentration in Computer Engineering and a minor in Electrical Engineering, achieving a cumulative GPA of 9.15/10.
Availability
Location
United StatesAuthorized to work in
Website
nitinjain.meJob categories
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