sujit noronha | Founding Machine Learning Engineer
AI Startup
San Francisco, CAOn-siteFull-time$140k – $220k / year
About this role
Sujit Noronha
Machine Learning Engineer at Kidco.ai | LLM Post-training & inference | AI Evals | MLOPS
San Francisco Bay Area (US)
500 connections • 3,266 followers
About
I am a dedicated Machine Learning Engineer with a focus on Natural Language Processing (NLP) and Computer Vision, currently pursuing a Master's degree in Natural Language Processing at the University of California, Santa Cruz (UCSC). In my prior role at Quantiphi, I served as a machine learning engineer, where I played a pivotal role in developing large-scale computer vision systems. My responsibilities spanned the entire development pipeline, encompassing research, development, and deployment. Currently, working on LLMs, Agentic Systems, and Multi-modal Architectures. I am deeply involved in optimizing machine learning models for edge devices, employing advanced techniques such as pruning, quantization, and knowledge distillation to enhance performance and develop sophisticated systems that can operate effectively in resource-constrained environments while maintaining high accuracy and reliability.
Total Experience: 4 years and 8 months
Experience
KIDCompany(https://www.linkedin.com/company/kidcompany)
Founding Machine Learning Engineer (Current)
Jan 2025 - Present (1 year and 6 months) in Palo Alto, California, United States
Department: Engineering and Technical • Level: Founder
Machine Learning Engineer
Jul 2024 - Dec 2024 (5 months) in Palo Alto, California, United States
Led the end-to-end development of a voice-based AI hardware device that enables children to craft interactive stories, generate digital artwork, and compose original music through natural conversation.
- Architected and implemented an end-to-end voice-based interaction system leveraging Google Cloud Platform for LLM orchestration
- Engineered a low-latency cloud infrastructure for streaming Text-to-Speech (TTS) audio outputs using websockets, enhancing user experience
- Implemented compreh