See how CoreWeave Physical AI Field Engineering brings AI and domain expertise together to solve complex engineering challenges. Engineers with backgrounds in automotive, aerospace, and mechanical engineering work directly with teams to build, validate, and deploy AI models using real-world data.
Learn how this approach turns simulation output, test results, sensor data, and telemetry into production-ready models—validated against real physics and designed to give engineering teams long-term control over their data and AI.
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Engineers who really understand their domain
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usually aren't the same engineers
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that can build production-ready AI solutions.
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The combination of these skills
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is incredibly hard to find.
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I've only really seen a handful
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of such engineers over the last decade.
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Meanwhile, companies are wasting time
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as new AI solutions to their old problems
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allow startups to move a lot faster.
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Physical AI Field Engineering is a team of
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experienced specialists who spent
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years developing AI solutions
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that don't end up in POC purgatory,
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but actually make it into production.
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We even try to match domains,
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so that you get a specialist who’s
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built solutions for automotive engineering
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if your problem is in that particular area.
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What makes our team unique
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is their background in the industry.
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So this team of specialist field engineers
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has joined with a deep knowledge
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of the automotive
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and aerospace industry.
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And they are able to really understand
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the domain engineers’
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workflow and get to where
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they should be applying
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AI to see the highest value.
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Back in the days,
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all of us were mechanical engineers
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or aerospace engineers, including myself.
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We try to work very closely
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with our customers.
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The best way to do
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that is sat in the same office,
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at the same test rig as the engineers
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who are actually doing the work.
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We approach solving their problems
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as an engineering problem,
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that we're using machine learning
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and AI to solve for them,
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rather than a machine learning or
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AI problem that we're solving for them.
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The end product of our engagement
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is to give them a platform
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that they can use in their workflows,
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surfacing all of the insights,
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getting the outputs
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that they are looking for.
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Usually that involves either
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a reduction in the time
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spent doing physical
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testing and improvement
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to the quality of the products
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that they’re able to achieve.
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We don't want to build another
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consulting company.
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We just want to
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accelerate the progression
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of science and enable enterprises
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to utilize the latest tech in AI
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to solve their hardest challenges.
