Bullseye search · ML & AI Engineer
Plenty of people can train a model. Far fewer can ship one, measure it, and keep it fast and affordable in production. I run a bullseye search for the engineers who have done the second part.
The short answer
Chasen Talent is a founder-led recruiting firm that finds ML and AI engineers for early-stage to growth startups. Founder Chasen Nick runs every search personally as a bullseye search: one specific profile, recruited directly from teams where that work is happening now, and verified before a client ever sees them. In 2026 he has worked 14 ML & AI engineer searches, with base salaries from $140K to $400K.
2026 market read, from my desk
Most requested: Python, PyTorch, Kubernetes and Docker. Figures come from real searches on my desk this year. Client names stay confidential.
The target
Has shipped models real users depend on, not research-only work.
Can explain latency, cost, and eval results for what they built.
Trains in PyTorch and understands the infrastructure that serves it.
Builds the pipelines and evals that make the next model better.
Where I find them: Applied ML teams at product companies, autonomy and robotics groups, recommendation and ranking teams, and AI startups where the model is the product.
How a bullseye search works
We pin down the exact profile together: what they've shipped, the stack, the stage, and the dealbreakers. No keyword lists.
I map who's doing this work right now and recruit them directly with your specific role. Most of them are not applying anywhere.
I check real track record and current role before anyone reaches you, so you meet finalists, not a stack of resumes.
Every step is run by me, Chasen Nick. No handoffs to junior recruiters. How that compares to a big agency →
Questions founders ask
Across the 14 ML & AI engineer searches I've worked in 2026, base salary ran from $140K to $400K, with a typical midpoint around $210K. Equity, where stated, ran 0.2% to 0.75%. 12 of the 14 were based in the San Francisco Bay Area, 2 in New York, and 8 were in-person roles.
In practice, ML engineer usually means training and deploying models, while AI engineer often means building products on top of foundation models (agents, retrieval, LLM features). Many searches want both, so I pin down which one the role really needs before sourcing.
An ML engineer optimizes models that ship in a product. A research engineer builds the experiments, benchmarks, and environments that decide what to build next. See research engineer search.
California, New York, and remote across the United States. Most searches on my desk right now are in-person roles in San Francisco.
Other bullseye searches
Tell me about the role. If I'm not the right fit for it, I'll tell you that too.
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