For employers
An AI infrastructure recruiter who maps the pool by name.
GPU cloud, fabric, platform, SRE and MLOps seats are filled from pools small enough to enumerate. We build that map, run the evidence-based screen, and hand you a shortlist plus the research it came from.
Coverage
The five functions we recruit against
Treating these as one requisition family is the single most expensive mistake in AI infrastructure hiring. They are separate pools with separate evidence.
GPU cloud & fleet
GPU infrastructure engineers, fleet reliability, cluster operations, capacity planning
Screened on what they did the last time a cluster degraded and the cause was outside their layer, not on the frameworks listed on the resume.
AI networking
InfiniBand and RoCE fabric engineers, network architects, datapath performance
A small, named pool. These people are found by mapping who has actually run a non-blocking fabric at scale, then approached directly.
Platform & SRE
Kubernetes platform engineers, scheduler and queueing owners, SREs with a real mandate
The failure mode is hiring SRE as a title with no error-budget authority. We qualify the mandate before we qualify the candidate.
MLOps & training infrastructure
Training platform engineers, MLOps, data and checkpoint pipeline owners
Distinct from platform, and expensive to absorb into it. We separate the requisition before the search starts.
Facility interface
Critical facility engineers, liquid cooling, electrical capacity leads
The seat where compute meets the building. Hired from data center operations, not from software.
How the search runs
Research first, outreach second
Every engagement produces a written market map you keep, whether or not you hire through us.
Intake against the system, not the job description
What the cluster is, what breaks, who owns the layer above and below, and what the first ninety days actually require. This is where a mis-scoped requisition gets caught.
Named-pool mapping
Supply density by metro, the specific companies running comparable infrastructure, and the adjacent pools worth opening, HPC labs, trading infrastructure, telco datapath.
Evidence screening
Incident-level questions and operator references. Candidates arrive with written notes on what they have personally owned, so your engineers interview signal instead of resumes.
Weekly written reporting
Pipeline, response rates, comp pushback and where the market is telling you the range or the scope is wrong. You act on the market read before the search stalls.
What the engagement requires from you
- A named hiring sponsor who can make decisions
- An approved compensation range before outreach begins
- Feedback on submitted candidates within 48 hours
- Interview slots held open during the active search window
We commit to delivery standards, research depth, outreach volume, reporting cadence and response times. Outcomes depend on market conditions, scope, compensation and process speed.
Questions
Working with an AI infrastructure recruiter
- What makes an AI infrastructure recruiter different from a general tech recruiter?
- The pools are small enough to be mapped by name. There is no keyword search that surfaces someone who has run a 4,000-GPU fabric, you build the map of who has, then approach them. That research is the work, and it is why generic sourcing stalls on these searches.
- Which roles do you recruit?
- GPU infrastructure and fleet reliability, InfiniBand and RoCE network engineering, Kubernetes platform and scheduler ownership, SRE, MLOps and training platform, plus the critical facility seats that keep the compute powered and cooled.
- How do you screen when you are not an engineer?
- With evidence questions written against the specific system, and with reference calls into the operator network. The strongest single question we ask: what did you personally do the last time the cluster was degraded and the cause was not in your layer? Vague answers do not survive it.
- How fast does a search move?
- Intake within days, a research map in the first week, first screened candidates usually inside two to three weeks on the named-pool roles. Fabric and capacity-planning seats run longer because the pool is genuinely small.
- What does it cost?
- Engagement scopes are listed on the homepage, including talent intelligence, embedded search, fractional talent partnership, site launch work and selective executive search. A hiring diagnostic confirms the right scope before any agreement is signed.
- Do you work with startups as well as hyperscalers?
- Yes. For an early team the more valuable engagement is often the market report first, supply density, real comp and adjacent pools, so the first two hires are designed rather than guessed.
Hiring on the facility side too? Data center recruiting covers the operations, commissioning and construction seats.
Keep reading
Related pages
Have a compute team to build?
Tell me the cluster, the stage and the seats. You get a market read before you get a pitch.