Your AI and data talent partner
The people who can ship a model or a pipeline to production are already employed, already well paid, and not reading job ads. We reach them through recruiters who have placed ML and data people before.
KiwiTechDigital product engineering for startups
BotsyncAutonomous mobile robots for factories
SkechersGlobal footwear brand
NoventiqDigital transformation & cybersecurity
OriginAutonomous construction robotics
FlentisContingent-workforce VMS platform
UNBOXROBOTICSUnbox RoboticsAI-powered parcel-sorting robotics
ManudataRobot-training data from live factory lines
J.CrewAmerican apparel retailer
Hola PrimeGlobal prop-trading firm
OpusingGlobal staffing & workforce solutions
Stefanini GroupGlobal IT services & consulting
Logic PlanetIT staffing & consultingRootStAffRootStaffExecutive search & recruitment
PSAG TechnologiesSAP consulting & IT servicesThree steps, in your language.
Training or inference? Notebooks or production? Warehouse or lakehouse? We pin down what “good” means in your stack — and the pitch to someone who already has a good AI job.
Screened against your rubric — real model and pipeline work, not tutorials — with comp expectations and notice confirmed before you see a name.
We run the panels, the take-home if you use one, the offer, and the notice period. Invoiced the day they join.
The AI & Data roles we work.
Three things that change when specialists work the role.
Specialists who know a fine-tune from a from-scratch run, and a platform owner from a notebook jockey.
Structured screens with a written case — what they built, at what scale, and what it cost.
AI talent gets counter-offered hard. Your recruiter stays on the candidate every week until day one.
The specifics, not the category.
Roles and stacks our recruiters in this domain work directly. If yours isn’t listed, it’s worth asking — the network is wider than the page.
Frequently asked questions
By screening for it explicitly. The gap in this market is between people who have trained models in notebooks and people who have run them in production, and a CV rarely separates the two. Our screen asks what was deployed, at what scale, what it cost to serve and what broke — and the written case records the evidence for each.
Yes. Live and recent mandates in this domain include applied AI and agent engineering, voice AI research, perception and egocentric vision research, and AI product leadership, alongside the standard ML engineer, data engineer and MLOps seats.
Mostly where the work ends. A data scientist is measured on the analysis and the model; an ML engineer is measured on the system that serves it and stays up. Briefing the wrong one is the most common reason an AI search stalls, so we pin down training-versus-inference, notebooks-versus-production and warehouse-versus-lakehouse in the brief before anyone is approached.
Reaching the right people is fast; closing them is not. AI talent is already employed, already well paid and gets counter-offered harder than any other domain we work. Budget for a 30 to 90 day notice period after acceptance, and expect the counter-offer conversation — the recruiter stays on the candidate weekly until day one.
8.33% of the hired person’s first-year salary — one month’s pay — invoiced on the day they join — the same success-fee terms as every other domain, with a 90-day replacement guarantee. No retainer and no fee if the candidate never starts.
Make hiring the thing you’re good at.
Seed, Series A to D, or enterprise — send us one hard role. We'll match it the same day, and you'll meet a shortlist in days. Pay only when they join.