Low utilization
The hardware is installed. Fleet operations, scheduling and observability hires are what raise utilization.
Expertise
GPU cloud and platform recruiting
Hiring for the teams that turn installed accelerators into a reliable product: fleet operations, scheduling, fabric, telemetry, storage, SRE and capacity planning. The titles overlap with general cloud engineering. The qualifying experience is operating accelerated compute in production. We map the people who have run accelerated compute in production.
Team scope
A delivered cluster needs four teams before it can be sold: fleet operations to keep nodes healthy, scheduling to raise utilization, fabric engineering to keep collectives fast, and telemetry to catch degraded hardware before a customer does. Each is a separate hire from a small pool.
Clients
GPU cloud and neocloud providers, AI labs operating their own clusters, enterprises standing up internal training and inference platforms, and infrastructure vendors building customer-facing deployment and operations teams.
Triggers
Hiring triggers
Customer SLAs
SLAs in contracts mean SRE ownership has to be real, with people who have carried error budgets under physical-infrastructure failure modes.
Fabric bottleneck
Training jobs stall on congestion and nobody owns the interconnect. Few engineers have run these fabrics, and they receive many offers.
Power limits
Expansion is limited by available megawatts. Capacity planners decide how much of the pipeline can be sold.
Roles
Role families
GPU fleet operations
Fleet operations engineer, hardware deployment lead, GPU operations engineer, capacity engineer
Screened on fleet scale operated, burn-in and RMA discipline, and thermal/power event handling on accelerated nodes.
Scheduling & orchestration
Slurm administrator, Kubernetes platform engineer (GPU-aware scheduling), workload orchestration engineer
Separated by the scheduler they have run in production and the tenancy model they supported. Batch research clusters and multi-tenant clouds need different experience.
Fabric & network engineering
InfiniBand engineer, RoCE fabric architect, network automation engineer, DCI engineer
Verified on topology design, congestion tuning and collective-communication behavior.
Observability & telemetry
Observability engineer, telemetry platform lead, monitoring architect for accelerated compute
Assessed on GPU-specific failure detection: ECC, throttling, fabric degradation and actual utilization.
Storage and SRE
Storage engineer (parallel file systems), SRE, production engineer, capacity planning lead
Screened on data-path throughput experience, availability ownership under physical failure modes, and power- or supply-constrained capacity modeling.
Org design
Hiring sequence
Fleet operations and deployment hires start with the hardware delivery. Scheduling and platform hires follow as tenants are added. Fabric hires come before the next cluster expansion, SRE when availability goes into customer contracts, and capacity planning when power becomes the limit on growth. We map the sequence with you before sourcing anyone.
Method
Search process
- Step 1
Brief
A written role brief, a scorecard agreed with the hiring manager, and a check of scope, pay and location against the candidate market before outreach starts.
- Step 2
Market map
A named list of target companies across AI infrastructure firms, operators, EPCs and OEMs, built with AI tools and checked against people we already know.
- Step 3
Outreach
Direct outreach from Czarina, with a weekly report on how many people were contacted, who replied and why people passed.
- Step 4
Screening
Screens against the scorecard, a written case for each candidate including the risks, and their pay expectations on the record.
- Step 5
Offer
Offer planning, a read on counteroffer risk, and help with the resignation and any relocation through the start date.
- Step 6
Handover
Your team keeps the pipeline, research files, messaging and comp data. The work stays with you when the engagement ends.
What the engagement requires from you
- A named engineering sponsor who can run a technical panel
- An approved compensation and equity range before outreach
- Technical feedback on submissions within 48 hours
- Clarity on remote, on-call and travel expectations up front
We commit to delivery standards, research depth, outreach volume, reporting cadence and response times. Outcomes depend on market conditions, scope, compensation and process speed.
FAQ
Questions
- What is GPU fleet operations and who do you hire for it?
- The team that keeps thousands of accelerators producing useful work: hardware health, thermal and power events, RMA flows, node burn-in and fleet-wide utilization. We hire fleet operations engineers, hardware deployment leads and capacity engineers, screened on the scale of fleet they have run.
- Do you recruit for Slurm and Kubernetes scheduling talent?
- Yes. Scheduler behavior is one of the sharpest screens in this market: people who have run multi-tenant Slurm clusters or GPU-aware Kubernetes scheduling at scale are a small, identifiable pool, and candidates without that experience cannot answer it in detail.
- Can you hire InfiniBand and fabric network engineers?
- Yes: fabric architects and network engineers with real InfiniBand or RoCE deployments, rail-optimized topologies, congestion tuning and collective-communication awareness. We distinguish them deliberately from enterprise and campus networking backgrounds.
- What is the difference between observability in GPU cloud and ordinary monitoring?
- GPU fleets fail in ways ordinary dashboards miss: ECC errors, thermal throttling, fabric congestion, degraded collectives and silent utilization loss. The people we screen have built telemetry that catches those failure modes before customers do.
- How do you help with capacity planning hires?
- Capacity engineers in GPU cloud sit between hardware supply, power availability and customer demand. We screen for people who have modeled power-limited and supply-limited environments.
- Where does SRE fit in a GPU cloud organization?
- SRE owns availability commitments to customers: error budgets, incident response and the observability stack. In GPU cloud the incidents are often physical (thermals, fabric, power), so we screen for SREs who have handled hardware and facility incidents.
AI infrastructure recruiting
The wider layer: platform, SRE and MLOps hiring around the cluster.
GPU cluster engineer guide
What the role owns, what it pays and how to qualify candidates.
Data center recruiting
The physical layer underneath the fleet: power, cooling, facilities.
Hillsboro-Portland market read
Where Hillsboro's infrastructure engineers come from and what they are paid.
Next step
Bring the cluster roadmap and the open seats. You'll get role definitions, a sequencing plan and a talent read on each seat.