AI Infrastructure • Specialized Hiring
The shortlist for roles that don't show up on LinkedIn.
First candidates in days. Every name with a reason.
Conventional recruiting can't fill these roles.
We've built the map. We do the screening. You interview people who can do the job.
Hidden Talent
The right candidates aren't on job boards.
Resumes Lie
Their resumes don't show what they've actually built.
Generalist Noise
A generalist agency can't evaluate what they can't understand.
Piles, Not Shortlists
You end up with a pile, not a shortlist.
The infra engineering layer.
And the roles no one else can source.
GPU Cluster & Fleet
Large-scale fabric, provisioning, fleet ops
Networking
InfiniBand, RoCE, RDMA, in production
HPC
Workload scheduling, MPI, performance tuning
Storage
Distributed, NVMe, object, data center-scale
SRE & Platform
Reliability and orchestration at scale
MLOps
Training pipelines, model serving, research-to-production
Specialized & Non-Traditional Talent
Candidates conventional hiring systematically misses
Inside the Map
Flip a card to see what a shortlist name looks like before it reaches you.
Built GPU fleet diagnostics SaaS · Defense & Surgical Robotics
High-ownership R&D or foundational build roles, not CRUD or surface-level web work.
Architecting full-stack tooling for distributed GPU hardware, end to end.
Has spent recent roles outside pure AI infra, so a short ramp-up on the latest GPU/cluster tooling is likely.
A strong 0-to-1 architect, best for "build the platform" roles.
Flash array garbage collection · ML systems internship at top tech co
ML or infra roles with strong team culture and room to learn from peers.
Production C++ on storage internals plus genuine ML pipeline experience.
Still early career, so brings more raw technical strength than seniority.
A high-potential builder who'd thrive with a senior mentor on the team.
GPU-aware scheduler + InfiniBand RDMA · 40%→85% GPU utilization
Hands-on building at a problem-solving startup, after years of founder and consulting roles.
Built a production GPU scheduling + RDMA stack that cut training time 56%.
This work sits a couple of roles back in a varied career, so a quick technical screen confirms it's still sharp.
The exact profile conventional recruiting misses entirely.
End-to-end training, serving, monitoring · Insurtech
A collaborative team with junior-to-staff engineers working on complex problems.
Owns the full ML lifecycle from data pipelines to production observability.
Comes from a mid-size company, so may need a short ramp-up on larger-scale infra.
A reliable "research to production" owner.
From intake to shortlist in days, not weeks.
Intake
Define the real bar with your hiring manager, not the JD.
Map
Pull scored candidates from an existing supply map, not a cold search.
Screen
AI + human review + technical voice screen against your bar.
Shortlist
Each candidate with a logic trace, delivered in days.
Close
You interview, we iterate until the role is filled.
Everything you'd ask on the first call.
How are you different from the agencies we use?+
How do you screen for something as technical as InfiniBand at scale?+
What does the logic trace actually look like?+
What's your hit rate?+
What does "retained" mean in practice?+
How do you handle confidentiality?+
How fast will we see something?+
Remote or on-site? Global?+
60-day replacement.
If a placement doesn't work out, we run the search again. No conditions.
Tell us the roles you're stuck on.
We'll come back with a read on the supply and a plan to close them.
Book a 20-minute call →