COREWEAVE PHYSICAL AI FIELD ENGINEERING

AI built for engineers solving physical challenges

Build exceptional products faster with AI capabilities designed around your engineering workflows.

physical ai

Built for engineers, by engineers

Your engineering challenges demand AI that understands the physical world. Partner with CoreWeave Field Engineers to apply AI directly to your workflows and solve the engineering challenges holding your team back.

Test less, learn more

Use the data you already have to predict outcomes earlier, cut extra iterations out of the loop, and get exceptional products to your customer faster.

See what others miss

Surface crucial faults buried in complex signals while they're still cheap to fix, and flag the true anomalies worth investigating instead of dismissing them as noise.

Put more flow in your workflow

Get AI capabilities designed around the way your engineers already work, so your team can use them directly to streamline and improve workflows.

solution brief

Powered by an integrated AI solution built for engineering

Every Physical AI Field Engineering engagement runs on CoreWeave's integrated AI solution, purpose-built to address domain-specific engineering challenges. Track experiments, manage models, and explore data in one place, alongside algorithms built for test reduction, anomaly detection, fault-correlation, system optimization, and agent-building. All in one guided experience.

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Scale on a cloud tuned for your workloads

Underneath our AI solution you will find the CoreWeave Cloud, from NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs for simulation and rendering to the AI infrastructure needed to train your models at scale.

Our engineers build alongside your team

Every engagement starts with a scoping workshop, on-site with your team: mapping your engineering workflows, digging into your biggest pain points, and aligning on priorities and a realistic timeline before any model gets built. From there, our Physical AI Field Engineers, who've solved challenging problems across more than 100 projects, don't hand over a report and step back. They prototype the solution end to end, alongside your team, and stay involved until it's running in production, not just running in a demo, across four areas:

Strategy

Helping identify which problems are actually worth solving with AI, and which data is worth building on.

Simulation infrastructure

Helping stand up the GPU, storage, and simulation stack a specific use case needs. For full infrastructure design and scale beyond that, we connect you into CoreWeave's broader physical AI platform.

Real-world data

Turning scattered test, sensor, and production data into a model that predicts an outcome, catches an anomaly, or explains a failure, instead of leaving that signal buried and unused.

Agentic learning

Turning what a model finds into something that changes the physical world: a system recalibrated to run better, a fault caught and corrected before it becomes a failure, or a robot executing a trained skill your team built and deployed.

Pain points

We feel your pain. And cure it.

Our Physical AI Field Engineers bring proven experience across strategy, infrastructure, data, and deployment, then start with your specific challenge by listening first.

Pain point:
"We generate and collect data across simulations, test benches, and the field, but a fault could be hiding anywhere in it, and we'd never know."
Turn every data source into signal

Analyze every simulation run, test record, or production signal in full, not a sample, so a hidden fault surfaces before it reaches the field.

Pain point:
“Calibrating our simulator against reality takes months, and that knowledge lives in a few engineers’ heads."
Calibrate in hours, not months

Replace manual, iterative calibration loops with a model trained on your own data, cutting months of tuning down to as short as a day.

Pain point:
“We have hundreds of possible tests and simulations to run, and no clear way to know which ones actually matter.”
Run only the tests that matter

Rank every planned test or simulation by the information it would add, so time and budget go only to what actually counts.

Pain point:
“When a fault reaches the field, we check the channels we suspect first, but sometimes the real cause is hidden somewhere else.”
Point to the real cause

Rank the channel most closely linked to a fault, not just the one you'd suspect first, so your team gets to the real cause faster instead of chasing it manually.

OUTCOMES, NOT PILOTS

Proven impact across automotive, aerospace, and wider engineering programs.

Results from delivered engineering programs. Some customers were anonymized under NDA.

100+
Successful engineering projects across automotive, aerospace, and industrial applications.
12wks to 1 day
Faster calibration testing using data already on hand.
35%
Fewer characterization tests needed to validate best test candidates.
20x
Reduction identified in data capture needed to reach target accuracy.

Frequently asked questions

How does a CoreWeave Physical AI Field Engineering engagement work?

Where does your domain expertise go deepest?

Do we need data scientists with machine learning experience?

What data do we need to get started?

Explore CoreWeave Physical AI

Go deeper with blogs, technical guides, and on-demand video.

Video

CoreWeave Launches Physical AI Field Engineering to Turn Proprietary Data Into Production AI

Watch how CoreWeave Physical AI Field Engineering combines domain expertise and AI to build, validate, and deploy models using real engineering data.

Blog

Getting Physical with AI

Join Richard Ahlfeld, SVP for Physical and Scientific AI at CoreWeave, as he explores the transformative impact of AI-powered Physical AI.

Webinar

Physical AI Field Engineering in action

Turn existing build and test data into validated physical AI workflows

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AI that moves the real world

Everything physical AI teams need: integrated toolchains, massive data handling, and engineers who understand AI and physics, in one place.