Utilization is below what the board expects
The hardware is installed. Fleet operations, scheduling and observability hires are how utilization becomes a managed number.
Expertise
GPU cloud, network and platform recruiting, screened on scale operated.
Hiring for the teams that turn installed accelerators into a reliable product: fleet operations, scheduling, fabric, telemetry, storage, SRE and capacity planning. These titles overlap with general cloud engineering; the qualifying experience does not. We map the people who have run accelerated compute in production.
Installed hardware is not a product
The distance between a delivered cluster and a sellable cloud is a team: fleet operations keeping nodes healthy, scheduling making utilization real, fabric engineering keeping collectives fast, and telemetry that catches degraded hardware before a customer's training run does. Each of those is a distinct hire, and each pool is small enough to map by name.
Who we hire for
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
When GPU cloud teams call us.
Customers are signing availability commitments
SLAs in contracts mean SRE ownership has to be real, with people who have carried error budgets under physical-infrastructure failure modes.
The fabric is the bottleneck
Training jobs stall on congestion and nobody owns the interconnect. Fabric specialists are a tiny pool and they know it.
Power is the growth limit
Expansion is gated by megawatts, not demand. Capacity planning talent decides how much of the pipeline is sellable.
Role families
Five pools that share a vocabulary and nothing else.
GPU fleet operations
Fleet operations engineer, hardware deployment lead, GPU operations engineer, capacity engineer
Screened on fleet scale actually 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 are different instincts.
Fabric & network engineering
InfiniBand engineer, RoCE fabric architect, network automation engineer, DCI engineer
Verified on topology design, congestion tuning and collective-communication behavior, not enterprise networking tenure.
Observability & telemetry
Observability engineer, telemetry platform lead, monitoring architect for accelerated compute
Assessed on GPU-specific failure detection: ECC, throttling, fabric degradation and utilization truth, beyond standard application monitoring.
Storage, SRE & capacity planning
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
Sequenced against the hardware landing schedule.
Fleet operations and deployment capacity arrive with the hardware. Scheduling and platform follow as tenancy grows. Fabric depth before the next cluster doubles. SRE when availability becomes contract language. Capacity planning as soon as power, not demand, sets the growth rate. We map the sequence with you before sourcing anyone.
Method
How the search runs.
- Step 01
Calibrate
A written role brief, an agreed scorecard, and a market reality check on scope, comp and location before outreach starts.
- Step 02
Map
AI-assisted mapping across AI infrastructure companies, operators, EPCs and OEMs, layered on a network built one conversation at a time.
- Step 03
Engage
Direct, human outreach with weekly reporting on volume, response quality and the objections we are hearing.
- Step 04
Validate
Structured screens against the scorecard, written candidate cases including risks, and comp expectations on the record.
- Step 05
Close
Offer strategy, counteroffer exposure assessment, and resignation and relocation support through the start date.
- Step 06
Transfer
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
Buyer 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 actually 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 generic platform resumes do not survive that question.
- 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, not just cloud spend optimization.
- 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 with infrastructure-adjacent instincts, not pure application backgrounds.
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.
HPC & AI infrastructure engineer guide
Cluster, fabric and platform experience, screened for production scale.
Data center recruiting
The physical layer underneath the fleet: power, cooling, facilities.
Hillsboro-Portland market read
A live example of silicon-adjacent infrastructure talent supply.
Scaling a GPU cloud team?
Bring the cluster roadmap and the open seats. You'll get role definitions, a sequencing plan and a talent read on each seat.