About
I train custom models and recruit the people who build them.
Most of my work sits around applied machine learning: training domain-specific models, building retrieval and embedding systems, and turning messy real-world data into systems that can reason usefully inside a narrow domain. I work with transformers, graph neural networks, contrastive embedding models, and the infrastructure needed to make them reliable outside notebooks.
Before that, I spent years in security research and built a cybersecurity company from the ground up. That background still shapes how I think: assume systems fail in strange ways, test the edge cases, and care about what happens in production more than what looks good in a demo. Recruiting came later, but it sharpened a different part of the same problem: recognizing the engineers and technical leaders who can actually build under pressure.
Professional Journey
June 2025 - Present
New chapter. Moved beyond pure niche headhunting into full-spectrum talent acquisition — supporting hiring across engineering, operations, and business functions for one of the largest water and hygiene technology companies on the planet.
Part of the reason I wanted to work inside an organization at this scale was to understand the complex problems large companies deal with up close: the small operational frictions, talent patterns, communication gaps, and cultural decisions that are easy to miss individually but end up shaping how an organization actually works.
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 in retained executive search 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. The work went far beyond sourcing: building market maps from scratch, understanding how leadership teams are structured, calibrating with boards and senior stakeholders, assessing executive fit, and approaching people who were not actively looking to move.
Executive search taught me how senior hiring really works when the stakes are high and the candidate pool is small. Most of the job is pattern recognition: knowing which signals matter, which narratives are noise, and how to evaluate whether someone can actually lead inside a specific business context. Built the kind of network you can only build by being 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 started. I taught myself security by taking real systems apart: reverse engineering binaries, writing exploit chains, and finding vulnerabilities in production software used at serious scale. Reported issues across Google, Apple, Meta, Microsoft, Amazon, and Uber under the handle "xedro", and I still help startups, open-source projects, and engineering teams find and fix the failure modes that matter. What stayed with me was not the thrill of breaking things, but the discipline behind it: tracing assumptions, finding edge cases, and understanding why systems fail when they meet reality. That way of thinking still sits underneath how I build models, debug infrastructure, and judge technical work.
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.
”The task is not so much to see what no one has yet seen, but to think what nobody has yet thought about that which everybody sees.”
— Arthur Schopenhauer
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.