Neil Noronha
Software, data engineering and artificial intelligence
I build products and am drawn to teams and companies that ship fast, iterate quickly, and learn directly from users.
I'm obsessed with extracting maximum performance from everything I do—software and data engineering professionally, running and football personally. I read extensively (Paul Graham is a favorite, though we disagree on plenty—happy to debate).
Right now, I'm building Nidra: a training optimization tool for athletes. It aggregates data from Whoop, Apple Health, and network activity to surface actionable insights that break bad habits and build better ones. I'm dogfooding it myself while training for two half marathons and a full marathon this year—if I can't make it work for me, it won't work for anyone.
Me Doing Things! ▼
Building
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Nidra
Training optimization tool that aggregates Whoop, Apple Health, and network activity to surface actionable insights for breaking bad habits and building better ones. Currently dogfooding while preparing for marathon season.
Startups That I've Built At!
- Jeeva
- Upgift
Experience
- Software Engineering Intern at Jeeva May 2025 – August 2025
- Data Engineer at Deloitte USI September 2023 – May 2024
- Data Engineering Intern at Deloitte USI January 2023 – April 2023
- Software Engineering Intern at General Electric June 2022 – August 2022
Projects
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ReKog
Photo album with natural language search—"find photos of dogs on beaches"—powered by AWS Rekognition and Lex. Fully serverless (Lambda, S3, OpenSearch). Users upload, AI labels, conversational search works instantly.
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DiningBot
AI dining concierge that understands "Italian near Union Square under $50" and returns ranked Yelp results. Lambda + Lex backend, responsive frontend. Zero-to-recommendation in seconds.
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LibSys
Multi-role library management system handling 10,000+ books with customer/author/admin workflows. PHP 8 + MySQL REST API supporting cataloging, events, and room reservations at scale.
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CycleForge
Cycle-accurate RISC-V processor simulator for performance analysis. Multi-stage pipeline with complete RV32I support, surfaces execution bottlenecks and timing metrics for optimization.
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ParkinSense
Wearable for Parkinson's patients that detects tremors (3–5 Hz) and dyskinesia (5–7 Hz) via real-time FFT on accelerometer data. LED feedback loop lets patients self-monitor symptoms without clinic visits.
Education
- NYU Tandon School of Engineering 2024 – 2026
- Manipal Institute of Technology 2019 – 2023
Hobbies
Tech Funbies
- Whoop
- Coros Pace 3
Essays
- On the Power of Simplicity May 2025
Inspired You?
Let's build something together.