About
I train custom models and recruit the people who build them.
Most people pick a lane. I kept switching — security researcher, startup founder, executive recruiter, ML engineer — and every time it looked like a wrong turn, it turned out to be a shortcut. Hacking systems taught me how the strongest engineers actually reason under pressure. Building a company from nothing and selling it taught me exactly what makes teams scale — and what quietly kills them. Interviewing hundreds of executives taught me the gap between people who lead and people who just hold the title. Training domain-specific models — transformers, graph neural networks, contrastive embedding systems — gave me a framework for all of it: a way to formalize the patterns that experienced people feel in their gut but can never quite articulate.
None of this was planned. I just kept following the most interesting problem in the room, and the problems kept getting harder.
Professional Journey
June 2025 - Present
New chapter. Moved beyond pure niche headhunting into full-spectrum talent acquisition — leading hiring across engineering, operations, and business functions for one of the largest water and hygiene technology companies on the planet. The interesting part: building Global Capability Centers in India from scratch. Org design, talent pipelines, cross-cultural integration — the kind of ambiguous, high-stakes problems where most playbooks break down.
February 2022 - May 2025 · 3 years 4 months
Three years running retained niche headhunting at one of the world's top leadership advisory firms. Placed C-suite and VP-level leaders across aerospace, defense, IT, pharma, and life sciences for Fortune 500 clients. Most of the work is mapping entire executive talent pools in niche technical domains and convincing people who aren't looking to make a move. Built the kind of network you can only build by being genuinely useful to very senior people over a long period of time.
2020 - 2022 · 2 years
2016 - 2020 · 4 years
This is where I learned what building actually means. Co-founded a cybersecurity company to replace the legacy signature-based detection that every enterprise was stuck on. We built the real thing — cloud-native architecture, ML-driven anomaly detection, behavioral analysis engines, neural nets for threat classification, fully automated incident response pipelines. Raised $12.8M from Tier-1 VCs and angels who'd been executives at Microsoft, Amazon, and Google.
Scaled from a prototype in a room to a 15-person team closing mid-six-figure enterprise contracts. I set the hiring bar, built the eng culture, ran the GTM motion, learned to translate deep technical roadmaps into language that boards and enterprise buyers actually cared about. Exited through a strategic acquisition that validated the architecture and the bet. Four years of the hardest, most useful education I've ever had.
2015 - Present
Where it all started. Taught myself to break things — reverse engineering binaries, writing exploit chains, finding zero-days in systems built by the best engineers in the world. Reported vulnerabilities across Google, Apple, Meta, Microsoft, Amazon, and Uber. Known in the security community as "xedro". This wasn't a career move — it was an obsession with understanding how things fail. That attacker's instinct — assume everything is broken until proven otherwise — became the foundation for every single thing I've done since.
My GitHub contribution graph reflects that I still ship code regularly, even as my work has shifted deeper into leadership and talent.
What I Work On
Glide is my main build — a career intelligence platform powered by custom-trained models, not off-the-shelf APIs. The system runs on a heterogeneous model ensemble: domain-specific transformers trained on 4.2B tokens of career data, a heterogeneous graph neural network for relational matching across a knowledge graph of 82M+ companies and 230M+ jobs, custom contrastive embedding models for career-domain semantic search, and RLHF-aligned generative models for synthesis tasks. 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. Read more about why I’m building it →
On the research side, I focus on making custom models reliable in production — not just on benchmarks:
- Domain-specific pretraining — continual pretraining and full-parameter fine-tuning where LoRA hits its ceiling
- Graph neural networks — heterogeneous GNNs for relational reasoning over career knowledge graphs
- Preference optimization — GRPO-based alignment that eliminates reward models while reducing reward hacking
- Efficient serving — speculative decoding, quantized encoders, drift-aware retraining pipelines
I also advise companies on technical hiring strategy and building leadership teams that can execute on ambitious roadmaps.
How I Think
I start with one question: what’s actually happening here? Not what should be happening, not what the docs say — what’s actually going on in the system.
This applies equally to debugging a training run, evaluating a candidate, or designing a model architecture. Break it into testable pieces. Watch how the parts interact over time. Trust data over narratives. Optimize for resilience, not elegance.
The most interesting problems sit at the edges between domains — where custom model training meets human judgment, where research becomes deployed infrastructure, where individual decisions quietly shape organizational outcomes.
”No man ever steps in the same river twice, for it is not the same river and he is not the same man.”
— Heraclitus
The most interesting work lives in overlaps that don’t fit neatly on a résumé. My path from security research to startup to recruiting to ML wasn’t planned — each phase just built skills that compounded on the last. I follow the questions that hold my attention, especially at the boundary between people and systems, and let the labels catch up later.