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Shubhankar Kahali

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I build things at the intersection of ML and people systems. I started in offensive security — reverse engineering binaries, writing exploit chains, finding zero-days across Google, Apple, Meta, Microsoft, Amazon, and Uber. That led to Hyperpage, a cybersecurity startup I co-founded — ML-driven threat detection replacing legacy signature-based approaches with neural nets for threat classification and automated incident response. Raised $12.8M, scaled to enterprise contracts, and exited through acquisition.


Now I build production foundation models for sequence understanding — transformer architectures, attention mechanisms, few-shot adaptation — where reliability matters more than benchmark scores. I also do executive search, placing senior engineering leaders at Fortune 500 companies. The pattern recognition transfers well — evaluating whether a system will hold under pressure is the same skill whether it's a neural network or an engineering org.


My side project Glide Logo is where these converge. It started as a matching engine — transformer-driven, distributed microservices, real-time ML pipelines, ensemble methods with deep collaborative filtering — and has grown into a full career intelligence platform. The system analyzes 200+ data points per candidate, indexes 82M+ companies and 230M+ jobs, and surfaces what job seekers actually need: how companies operate internally, hiring patterns, org-level signals, and hidden dynamics that never show up on a careers page. 444k+ users, 94% match accuracy, 81% interview success rate, $554M+ in total salary value delivered across 36.9k+ placements — with users averaging a 30% salary increase and offers closing in 16 days.


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