The AI roles we recruit for
Six very different jobs that all say AI on the tin.
An AI researcher and an inference infrastructure engineer share an acronym
and not much else. They read different signals, care about different things and
need a different pitch, so the agency runs each of these as its own search.
01
AI researchers
Research scientists pushing on pre training, post training, alignment, evaluation or reasoning, with the publication record to back it up. Found in the published record, months before the doctorate ends.
PhD or equivalent
Publications
Frontier labs
02
Applied AI engineers
The bridge profile, often a researcher who moved into industry. They turn a promising idea into training runs that finish, experiments that reproduce and code other people can use.
PyTorch
Distributed
CUDA
03
AI engineers
Engineers who get models in front of users and keep them there: agents, retrieval, tool use, evals, and the cost and latency work nobody demos. Found through public code and the middle of author lists.
Agents
Evals
Product sense
04
ML and inference infrastructure
The people who own the GPUs, the serving stack and the throughput numbers, and who keep an inference bill from quietly eating your runway.
Serving
GPU fleet
Reliability
05
Founding and early engineers
Broad builders for AI native startups, comfortable when the spec does not exist yet and happy to own something end to end.
0 to 1
Full stack
Ambiguity
06
Heads of AI and CTOs
Leaders who can set an AI roadmap, hire the team that delivers it, and stay close enough to the work that good engineers respect them.
Leadership
Roadmap
Hands on