COREWEAVE PHYSICAL AI FIELD ENGINEERING

AI built for engineers solving physical challenges

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

Let's talk about your project Explore our solutions

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.

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.

Find out more

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.

Explore the Physical AI loop on CoreWeave Cloud

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.

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.

100

Successful engineering projects across automotive, aerospace, and industrial applications.

wks

day

Faster calibration testing using data already on hand.

Fewer characterization tests needed to validate best test candidates.

Reduction identified in data capture needed to reach target accuracy.

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

Copied

How leading teams are putting physical AI to work

Formula 1 team turns F1®radio chatter into better race-day decisions

During a Grand Prix, Aston Martin Aramco Formula One™ Team can access every competitor's radio traffic, but no one can listen to 40 channels at once and still run a race. Working with CoreWeave engineers, the team built a tool that transcribes every channel in real time, tags what's being said by topic, and surfaces it fast enough to act before a pit window closes.

Read the story

Cadillac Hertz Team JOTA tests less, learns more

CoreWeave's field engineers have worked alongside Cadillac Hertz Team JOTA for a few years, building custom AI tools for their race-day workflows. During suspension testing on JOTA's seven-post rig, they built a tool that reads test data and recommends which setup to try next. In an early pass, several of its picks landed toward the stronger end of the field, a strong first signal as the team heads into further testing.

Read the story

The reason this collaboration works is that we're experimenting together, not just being handed a tool. CoreWeave understands engineering as well as AI, so it never feels like we're explaining our sport to them."

Tomoki Takahashi, Technical Director

JOTA Group

Nissan reduces physical vehicle tests by 17%

Nissan’s engineering teams are using AI to replace expensive physical prototype runs, pairing existing historical test data with machine-learning predictions across the vehicle development cycle. A pilot cut physical tests by 17%, and cut total testing time throughout Nissan’s European operations.

Read the story

The approach [from CoreWeave Physical AI Field Engineering] not only accelerates our time to market but also supports our commitment to innovation and sustainability. As we look to the future, AI will play an increasingly central role in how we design, test, and deliver the next generation of vehicles to our customers sooner.”

‍

Emma Deutsch, Director of Customer Orientated Engineering and Test Operations

Nissan Technical Centre Europe

Frequently asked questions

How does a CoreWeave Physical AI Field Engineering engagement work?

It starts on-site. A use case discovery workshop maps your engineering workflows and identifies where AI creates the most value. Or, if you already have a specific use case, a technical feasibility workshop assesses your data and validates the approach. From there, our engineers work alongside yours to explore your data, build models validated against the physics of your systems, and deploy them as tools inside your existing workflow. You finish with a working application your team operates directly.

Where does your domain expertise go deepest?

Our field engineers come from mechanical, automotive, and aerospace engineering, with 100+ projects delivered for OEMs and Tier 1 suppliers. We go deepest in automotive, robotics, and engineered products and are extending the same approach into other industrial sectors and infrastructure operations. Across all of these, the same core challenges repeat: test reduction, calibration, anomaly detection, fault correlation, and system optimization.

Do we need data scientists with machine learning experience?

No. But if you have them, we help them move faster. When your team is new to AI , we build with your domain engineers and deploy applications they run directly. When you already have data scientists, we augment them with infrastructure, experiment tracking, and specialists who work inside your existing pipeline.

What data do we need to get started?

The data you already generate: test-bench, calibration, simulation, production, or field data. Historical test data is often enough to start. The first workshop assesses exactly what you have and what it can support.

How is this different from an AI consultancy?

Consultants deliver recommendations. We deliver working applications built by engineers who’ve solved these problems themselves, embedded in your workflows, and powered by CoreWeave's own compute, applications, and solutions. It runs as a working app inside your day-to-day operations.