About
I solve problems at the intersection of human intelligence and machine learning systems. My work spans three domains: technical recruiting, ML research, and building AI products.
What makes me different is my approach to understanding systems—whether they’re neural networks, organizations, or markets. I don’t just study them; I break them down, rebuild them, and figure out how they actually work under pressure.
My technical foundation runs deep, built through hands-on experience across security research, entrepreneurship, and talent systems:
Professional Journey
June 2025 - Present
Recently started exploring a new dimension of talent acquisition beyond pure executive search. Leading both technical and non-technical hiring initiatives across engineering, operations, and business functions for complex industrial and sustainability-focused technology solutions.
Building Global Capability Centers (GCCs) in India while solving strategic talent challenges that extend far beyond traditional headhunting—from organizational design and talent pipeline development to cross-cultural team integration and scaling challenges in emerging markets.
February 2022 - May 2025 · 3 years 4 months
Executive headhunter specializing in retained search mandates for Fortune 500 clients including TE Connectivity, Eurofins, OpenText, and Takeda. Focused exclusively on C-suite, VP-level, and senior director executive placements across aerospace, defense, IT, and pharmaceutical sectors.
Conducted deep market intelligence and executive talent mapping to identify and recruit passive senior leaders within highly specialized technical domains. Built extensive networks of executive contacts and maintained confidential relationships with top-tier leadership talent.
2020 - 2022 · 2 years
2016 - 2020 · 4 years
Co-founded a disruptive cybersecurity startup that challenged traditional enterprise security models through radical cloud-native architecture. Built bleeding-edge threat detection systems that identified zero-day vulnerabilities before they hit the wild, fundamentally reimagining how enterprises think about proactive security.
Raised $2.3M across multiple rounds from battle-tested angels (ex-Microsoft, Amazon, Google executives) and Tier-1 VCs who bet on our vision to democratize enterprise-grade security. Moved fast and broke things while maintaining paranoid-level security standards.
Scaled from garage startup to 15-person team generating $100K+ enterprise contracts, proving that scrappy innovation could compete with legacy security giants. Executed successful exit through strategic acquisition, validating our contrarian approach to cybersecurity infrastructure.
2015 - Present
What started as curiosity about how systems break has evolved into a deep passion for information security that drives everything I do. Discovered vulnerabilities across major platforms including Google, Apple, Meta, Microsoft, Amazon, and Uber through systematic reverse engineering—not for bounties, but because understanding security flaws at this level feeds my obsession with system resilience.
This work has become essential to who I am as both a technologist and entrepreneur. The mindset of thinking like an attacker while building defenses shapes how I approach every technical challenge, from startup architecture decisions to evaluating security-conscious leadership talent. It's the foundation that connects all my other work.
My GitHub contribution graph reflects my continued commitment to code development despite my career transition.
What I Work On
I’m building Glide, an AI-powered platform that matches technical talent with opportunities using deep learning models trained on hiring patterns, technical assessments, and career trajectories. It’s where my recruiting experience meets my ML research—creating systems that understand both human potential and technical requirements.
My research focuses on making large language models more reliable and efficient. I’m particularly interested in:
- Inverse scaling phenomena: Why some models get worse as they get bigger, and what this tells us about intelligence
- Reasoning architectures: Building systems that can think step-by-step without sacrificing speed
- Deployment challenges: The gap between research breakthroughs and production systems that actually work
I also advise companies on technical hiring strategy, helping them build teams that can execute on ambitious technical visions.
How I Think
I approach problems by first understanding the underlying system dynamics. Whether I’m evaluating a candidate, debugging a model, or designing an architecture, I start with the same question: What are the forces actually at play here?
This means:
- First principles thinking: Breaking complex problems down to their fundamental components
- Systems perspective: Understanding how individual pieces interact and influence each other over time
- Empirical validation: Testing assumptions against real data, not just theoretical models
- Long-term optimization: Building for sustainability and adaptability, not just immediate results
I work best on problems that sit at the intersection of multiple domains—where technical depth meets human psychology, where research insights become practical systems, where individual decisions compound into organizational outcomes.
”Things just happen to happen. They don’t happen because they’re supposed to.”
— Benjamin Hoff, The Tao of Pooh
I’ve learned that the most interesting opportunities often emerge from unexpected combinations of experience and circumstance. My path from security research to recruiting to ML wasn’t planned, but each phase built capabilities that inform the others. I stay curious about where the work leads, rather than trying to force it into predetermined categories.