AI/ML Executive Search in New York

New York work skews towards research, and towards companies that cannot yet say who they are. That inverts the usual difficulty. The constraint is not finding the right person, it is that a company still in stealth has no brand to open the conversation with, so the approach itself has to carry the weight.

Where a role sits at a specific research intersection, the realistic pool is frequently a few hundred people worldwide. Most are mid-PhD or settled in research posts at major industrial labs, and almost none of them are looking. A New York address does not shrink that problem. It means the person will probably have to move, which is a conversation to have at kickoff rather than at offer.

The other difference is who you are bidding against. In the Bay Area the alternative offer usually comes from another lab. In New York it often comes from a quantitative fund or a trading firm, which pays on a different basis entirely. Benchmarking a New York research offer against West Coast startup data is one of the more expensive mistakes we see.

Early research labs, frequently in stealth

Series A and seed companies with tier-one backing, hiring their first engineer before they have a public name to open a conversation with. The hardest searches in the city and the ones we do most.

Applied AI product companies

Post-Series-A companies putting models into a product that customers pay for, hiring machine learning engineers and forward deployed engineers who can work in front of a client.

Established companies building an AI function

Media, health, insurance and professional services firms standing up an AI team from nothing, usually starting with the leader and building down.

The founding hire came out of a pool of a few hundred people worldwide, sitting at an intersection of world models and robotics trained on a data type almost nobody holds at scale. Shortlist in one week, offer in three. The person was given the title Head of Robotics Research early on and now leads a multi-phase roadmap.

The follow-on search should have been easier and was not. The lab needed a research bench, and the people who could build it were mid-PhD or settled in posts at major industrial labs. They had already declined the conventional version of the offer. Approaching the same small pool again with the same proposition would have produced the same answer.

So we changed the offer rather than the outreach, and built the engagement as a part-time research residency. Three researchers joined on that basis, and both founders have since committed to hiring the rest of the bench through us exclusively. That is the New York pattern in one client: when the pool is small enough to count, the shape of the role is a variable, not a fixed input.

The person who takes your brief is the person who maps the market, makes the approaches, sits in the first-stage interviews and handles the offer. Nothing is written up and handed to somebody else, which is why what you say in the kickoff call still shapes the shortlist three weeks later.

On a stealth search we agree at the outset what may be said and at what point in the process your name is used. That is your decision throughout, not ours.

Two things we settle at kickoff rather than at offer stage, because both cost weeks if they surface late: whether you will sponsor a visa, and whether you will fund relocation. On a small global pool, the answer to the second one frequently decides whether the search is possible at all.

Two pieces worth reading before you engage anyone. AI/ML recruiters in New York and the US covers how the three US cities differ, what you are really competing with, visas, relocation and the questions worth asking any firm. The best AI/ML executive search firms in 2026 compares the seven firms that come up most often, including us, and says where we are the wrong choice.