Hiring nowAI observability, reliability and FinOps

AI Capacity Planning Engineer

Also called ML Capacity Engineer · Compute Capacity Planner · Compute Economist · GPU Allocation Lead

An AI Capacity Planning Engineer plans how much compute the company needs, when, and at what cost. They keep services reliable at peak load without paying for GPUs that sit idle.

What they do

They forecast demand from product plans, customer commitments and training schedules, then turn it into a model of the compute needed. They study how inference and training workloads queue, batch and fail under pressure. They read telemetry, traces and cost dashboards to see where capacity is really used. They run load tests and build stress scenarios for launches, traffic spikes and hardware loss. They work with infrastructure, research, finance and product to agree allocations and flag shortfalls early. They check past forecasts against what happened and fix the model.

What a strong hire delivers

In the first months, a strong hire gives the company one trusted capacity model that engineering and finance both use. They set out stress-test scenarios for the likeliest peaks and failures, with clear findings. They cut the gap between forecast and actual use, and show where capacity is wasted or short. Teams get a repeatable way to request and justify compute.

How you can tell they are good

Ask them to walk through a forecast they built and how it fared against reality. Good candidates explain their errors without defensiveness. They can describe queueing behaviour, headroom and tail latency in plain terms. They know how GPU workloads differ from ordinary web services. They tie capacity choices to cost and reliability. Be wary of anyone who only talks about dashboards.

Why it is hard to fill

Few people have done this for AI workloads, so the title rarely appears on a CV. The work sits between SRE, performance engineering and finance, and most candidates are strong in one and thin in the others. Demand is high and the best people are held in place by labs and neoclouds that depend on them. Expect to hire for adjacent experience and judge the potential to learn.

Where to find them

Look first at SREs and performance engineers who have handled capacity for large, spiky services. Cloud operations and FinOps practitioners are the next pool, especially those who have worked with GPU spend. Some come from infrastructure planning at labs, neoclouds and large cloud providers. Also consider analysts from supply chain or operations research who have moved into technical teams.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Capacity Planning Engineer

Tell me about a capacity forecast you built. How did it compare with what actually happened?
A strong answer names the inputs, the method and the size and cause of the error. They explain what they changed afterwards. Worry if they claim it was accurate without evidence or cannot say how it was checked.
How would you plan capacity for an inference service whose traffic can double within hours?
Look for headroom, autoscaling limits, GPU start-up time, queue behaviour and load shedding. They should weigh cost against reliability. Worry if they only suggest buying more capacity or ignore how slowly GPUs become available.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We run AI services that customers rely on, and compute is our largest constraint. We are hiring a Capacity Planning Engineer to forecast demand, plan supply and make sure we have the right capacity at the right cost. You will work with infrastructure, research, product and finance.

What you will do

Build and maintain a capacity model for training and inference workloads
Forecast demand from product plans, customer commitments and research schedules
Design and run load tests and stress-test scenarios
Track forecast error and improve the model over time
Analyse telemetry, traces and cost data to find waste and risk
Advise on compute allocation and agree it with engineering and finance
Raise shortfalls early with clear options

What you will bring

Demonstrated capacity planning or performance work on a large production service
Working knowledge of queueing and demand forecasting
Experience reading telemetry, tracing and cost dashboards
Understanding of how ML training and inference workloads use GPUs
Ability to explain trade-offs between cost and reliability to non-engineers
Evidence of testing a forecast against real outcomes

Nice to have

Experience in FinOps or cloud cost management
Background in incident response and reliability engineering
Familiarity with model lifecycle and MLOps practice

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

Reliability and incident responseAdvancedDetect failures, restore service and prevent recurrence
MLOps and model lifecycleAdvancedVersion, release, monitor and retire models
Commercial and financial analysisWorkingAssess unit economics and value realisation
Model evaluationWorkingMeasure capability and failure modes credibly

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

AI Risk Management Framework, NIST
MLflow documentation, MLflow contributors
vLLM documentation, vLLM contributors

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