A cluster is landing before the team exists
Hardware arrives on a delivery date. Operations, fabric and platform coverage has to be in place to make it usable.
GPU and HPC recruitingAI Infrastructure Recruiting
AI Infrastructure Recruiting for the Teams Behind Compute
Critical Path Hiring recruits across the infrastructure layers that make AI compute available, reliable and usable: GPU and HPC clusters, network fabrics, distributed systems, storage, platform engineering, SRE, observability, MLOps, model serving and the physical data center systems underneath them.
Who this is for
GPU cloud and compute platforms, AI labs and model companies, enterprises building internal training and inference platforms, and data center operators adding compute and networking depth to an existing critical facilities organization.
Why these searches stall on titles
The same title covers very different work. A platform engineer at an AI company may own training orchestration. At an enterprise the same title means CI and Kubernetes upkeep. Searches that start from titles produce pipelines of plausible, unqualified candidates. Searches that start from ownership boundaries, scale operated and failure modes handled produce shortlists that survive a technical panel.
The core distinction
An AI engineer is not an AI infrastructure engineer.
These nine disciplines are routinely combined into one requisition. Each row is a different candidate market with different evidence, and the table is the fastest way to see where your role actually sits.
One requisition often contains four different talent markets.
Platform, reliability, deployment and customer-facing engineering are separate talent markets. Combining them into one job description is one of the reasons AI infrastructure searches stall.
| Discipline | What it owns | Qualifying evidence | Candidate market |
|---|---|---|---|
| AI application engineering | Product features built on top of models and APIs | Shipped user-facing capability, prompt and evaluation work, product judgment | Product and full-stack engineers |
| ML engineering | Model training, fine-tuning and evaluation quality | Model performance work, dataset and experiment discipline | ML and research engineers |
| ML infrastructure | The systems that training and evaluation run on | Pipeline scale, job orchestration, artifact and dataset lifecycle | Infrastructure engineers with model literacy |
| GPU and HPC infrastructure | Clusters, schedulers, fleet health and capacity | Node count and topology operated, failure recovery, utilization ownership | HPC, hyperscale and GPU cloud engineers |
| Platform engineering | The internal surface other engineering teams consume | Adoption of what they built, toil removed, interface design | Infrastructure and developer platform engineers |
| SRE | Availability outcomes and incident response | Error budgets, on-call design, postmortem and remediation record | Production engineers and reliability specialists |
| MLOps | Training and inference pipelines end to end | Checkpointing, rollout safety, utilization and cost ownership | Infrastructure engineers in ML environments |
| Model serving and inference | Latency, throughput and cost per request | Batching, quantization tradeoffs, tail latency measurements | Performance and systems engineers |
| Forward-deployed engineering | Customer deployments and integration outcomes | Work inside customer environments, technical credibility with buyers | Solutions, field and deployment engineers |
Triggers
When AI infrastructure teams call us.
Research or product is blocked on platform work
Model and product teams are maintaining their own pipelines. Platform and MLOps hires need to take that back.
MLOps and model serving recruitingReliability is now a customer commitment
Availability targets moved from internal aspiration into contract language, and reliability ownership has to be real.
Platform, SRE and observability recruitingTitles are producing the wrong candidates
The pipeline is full of cloud generalists. The scorecard needs rewriting before any more sourcing.
Request a talent market mapRole families
Seven distinct pools that share a vocabulary.
GPU and HPC infrastructure
Cluster engineer, capacity engineer, hardware deployment lead, fleet operations engineer
Screened on scale actually operated, scheduler and orchestration exposure, firmware and driver lifecycle, and failure handling on accelerated nodes.
Network and fabric
InfiniBand engineer, fabric architect, RoCE engineer, network automation engineer
Distinguishes high-performance interconnect experience from enterprise or campus networking, using collective communication and congestion behavior as the evidence.
Distributed systems and storage
Distributed systems engineer, storage infrastructure engineer, control plane engineer
Assessed on consistency and coordination reasoning, throughput at training scale, and checkpoint and restart behavior.
Platform engineering
Platform engineer, infrastructure engineer, developer platform lead, Kubernetes engineer
Owns the internal surface other teams build on, assessed on abstraction quality and adoption rather than tool familiarity.
Site reliability and observability
SRE, production engineer, reliability lead, observability engineer
Assessed on error budget practice, on-call design, incident ownership, and signal quality and cost under customer-facing targets.
MLOps and model delivery
MLOps engineer, ML infrastructure engineer, training infrastructure engineer, inference platform engineer
Separates pipeline and lifecycle ownership from data science: distributed training, checkpointing, serving latency, cost per request and GPU utilization.
Forward-deployed and solutions engineering
Forward-deployed engineer, solutions engineer, deployment engineer
Needs infrastructure credibility plus customer-facing judgment, which is a separate hiring market from internal platform work.
Calibration
Common role-calibration mistakes.
Each of these is fixable before outreach, and each one otherwise costs a hiring cycle.
- Writing one requisition across platform, reliability, deployment and customer-facing engineering.
- Screening for years of Kubernetes instead of for operating accelerated workloads under contention.
- Scoping an infrastructure role as a data science role and assessing modeling depth.
- Setting a compensation band from general cloud engineering data rather than accelerated compute evidence.
- Requiring a specific scheduler or serving framework when the real skill is capacity and performance reasoning.
- Hiring reliability ownership before there is a service and a target to be reliable against.
Sequence
What to hire first.
Sequence matters more than headcount. A typical order: one operations-capable engineer who can keep the cluster usable, then fabric and networking depth, then platform engineering to remove toil, then reliability ownership as availability becomes a commitment, then MLOps as training and serving stabilize.
Method
How Critical Path Hiring runs the search.
- 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
- An agreed panel or exercise format that respects candidate time
We commit to delivery standards, research depth, outreach volume, reporting cadence and response times. Outcomes depend on market conditions, scope, compensation and process speed.
Engagements
Related engagement options.
Research first, search capacity next, or a defined-capacity partner if the whole plan needs running.
FAQ
Buyer questions.
- What is AI infrastructure recruiting?
- Hiring for the layers between hardware and models: GPU and HPC clusters, network fabric, distributed systems and storage, platform engineering, site reliability, observability, MLOps, model serving and inference. The titles overlap with general cloud engineering and the qualifying evidence does not.
- How is an AI engineer different from an AI infrastructure engineer?
- An AI engineer builds product features on top of models. An AI infrastructure engineer makes compute available, reliable and affordable: clusters, fabric, schedulers, platforms, pipelines and serving. They read as similar on a resume and they are separate talent markets.
- How do you tell an SRE apart from a platform engineer or an MLOps engineer?
- By what they owned. Platform engineers build the internal surface other teams consume. SREs own availability, error budgets and incident response. MLOps engineers own training and inference pipelines, artifact and model lifecycle and GPU utilization. The scorecard is written against ownership, not the title on the resume.
- Do you recruit for GPU cloud and compute platforms?
- Yes: GPU cloud platforms, AI labs, and enterprises standing up internal training clusters. These employers compete for the same small population of people who have run large-scale accelerated compute in production.
- Can you help design the team, not just fill seats?
- Yes. Most engagements start with role and sequence design: which roles the stage actually needs, what to hire first, and which responsibilities should stay with your existing infrastructure team.
- Does AI pick the candidates?
- No. AI accelerates market mapping, research organization and drafting. Every candidate decision is made and documented by a person. AI assists. People decide.
- How do you compete on compensation against larger AI companies?
- By being accurate rather than optimistic. We report the compensation expectations heard in live conversations, then work the levers you control: scope, ownership, hardware access, remote policy and equity structure.
GPU and HPC recruiting
Cluster engineering, fleet operations, telemetry and scheduling.
Network and fabric recruiting
InfiniBand, RoCE and high-performance Ethernet engineering.
Platform, SRE and observability recruiting
Three separate markets that share a vocabulary.
MLOps and model serving recruiting
Pipelines, inference performance and forward-deployed engineering.
Controls, BMS and DCIM recruiting
The software-adjacent controls layer in the facility.
Data center recruiting
The physical infrastructure underneath the compute.
Standing up accelerated compute?
Bring the cluster plan and the gaps. You get role definitions and a talent read on each one.