We operate where applied AI actually gets built

Applied AI recruiting demands technical depth. Most firms don't have it. We do.

A technical network built through proximity, not outreach

Our network spans engineers, researchers, and designers shaping AI-native products across NYC and SF.

These are not candidates you find through job boards or keyword searches. They are builders behind LLM copilots, multimodal systems, video models, and agentic workflows already running in production.

We engage directly with people pushing applied AI forward, following their work across research papers, GitHub repositories, internal systems, and real-world deployments.

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Meet the team

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Liam O'Brien

Founder & CEO

Liam is the founder of StellarSearch and a former executive recruiter with over four years’ experience placing senior engineers and AI researchers into applied AI startups in NYC and San Francisco. He previously led executive search at Moonhub and has helped founders hire founding engineers, ML leads, and senior AI researchers from top AI labs.

Adam Hylton

Head Of Talent

Adam is our Founding Recruiter and Head of Talent at StellarSearch, with over ten years of experience in recruitment. He specialises in building long-term relationships with founders and candidates, and has helped AI startups hire ML and AI engineers, research engineers, and research scientists to gain a real competitive edge.

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We don’t judge talent by resumes

We understand the full stack of applied AI, from early research through to real-world deployment. That technical context is how we evaluate talent properly and why our placements work.

Retrieval pipelines

We look for engineers who understand how context is surfaced, ranked, and maintained in real systems. Retrieval is often where AI products quietly succeed or fail, so we prioritise candidates who have worked through those trade-offs before.

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Model training

We prioritise people who have trained models on domain-specific data and understand the decisions that shape outcomes. Not just how to train, but how to evaluate progress and recognise when an approach isn’t working.

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Fine-tuning

We look for builders who know when fine-tuning adds value, and when it introduces unnecessary complexity. That discernment matters especially when timelines are tight and systems need to stay stable.

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Deployment realities

We favour candidates who have supported systems after launch and understand the realities of latency, scaling, and failure. Experience in production environments is a strong indicator of long-term impact.

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FAQs

Frequently Asked Questions

Hiring for AI?

Tell us what you're building. We'll find the engineers who can ship it.

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