Manager, Field Engineering

CoreWeave•Sunnyvale, CA

10d•$157,000 - $231,000•Onsite

About The Position

The Physical AI Field Engineering team at CoreWeave brings AI into the engineering organizations that design, test, validate, and operate physical products. We work alongside customer teams to identify high-value problems, build solutions against their data and real-world constraints, and deploy software that engineers use in their daily workflows. As Manager, Field Engineering (Physical AI), you will build and lead the operating model for this specialist practice in your region. You will combine people leadership, technical judgment, customer delivery, and commercial understanding to make Physical AI Field Engineering a repeatable, high-performing part of CoreWeave’s Field Engineering organization.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, Physics, Mathematics, or a related technical discipline, or equivalent practical experience.
10+ years of experience in technical consulting, field engineering, product engineering, applied AI, or a related customer-facing engineering discipline.
5+ years of experience leading and developing technical teams in a fast-moving environment, with a track record of hiring strong people, setting a clear bar, and creating an environment where specialists can do their best work.
Sufficient technical depth in AI/ML applied to physical systems or engineering workflows to engage credibly with ML engineers, data scientists, controls engineers, simulation specialists, and engineering leaders, and to make sound judgments about data, modeling, validation, and deployment trade-offs.
Experience partnering across Product, Engineering, Sales, Marketing, and Field Engineering to align priorities and move work from customer signal to delivered capability.
Excellent written and verbal communication skills with the ability to clearly articulate complex technical topics to technical, executive, and customer audiences.

Nice To Haves

Experience in automotive, robotics, aerospace and defense, industrial, manufacturing, or another engineering-intensive industry.
Experience delivering or supporting AI solutions in embedded, on-site, production, or safety-critical environments.
Familiarity with modern ML platforms and workflows spanning experimentation, model management, data exploration, application development, and cloud deployment.
Capable of owning capacity models, hiring plans, creating technical career frameworks, delivery KPIs, or portfolio-level planning across multiple customer engagements.
Track record of converting repeated customer problems into reusable software, product capabilities, or standardized delivery patterns.

Responsibilities

Evolve the operating model for Physical AI Field Engineering within your region, from opportunity qualification and problem framing through data exploration, model development, deployment, adoption, and expansion.
Lead, coach, and develop a high-performing team of Specialist Field Engineers, Forward Deployed Data Scientists, and Forward Deployed Engineers, including hiring, workforce planning, career development, performance management, and succession planning.
Set the technical and delivery bar for customer engagements, ensuring that teams build solutions that are grounded in engineering criteria, validated against physical constraints, and deployed into workflows where they create measurable value.
Partner directly with strategic customers and senior stakeholders across automotive, robotics, aerospace, and industrial engineering to understand their priorities, shape technical strategy, and maintain executive confidence throughout delivery.
Work closely with Account Executives, Account Managers, Account Solutions Architects, Technical Solutions Managers, Product, Engineering, and Platform teams to qualify opportunities, allocate specialist capacity, and create a consistent customer experience.
Build mechanisms that make the team more effective at scale, including reusable engagement patterns, delivery standards, onboarding, enablement, technical reviews, and knowledge-sharing practices.
Establish a disciplined field-to-product feedback loop: identify recurring customer needs, distinguish reusable product opportunities from one-off delivery work, and translate field evidence into actionable recommendations for Product and Engineering.
Own the operating metrics that show whether the model is working, including customer value, time to deployment, software adoption, delivery quality, team capacity, expansion, and infrastructure or SaaS opportunities created through engagements.
Represent Physical AI Field Engineering internally and externally, helping CoreWeave explain how domain expertise, applied AI, and the CoreWeave platform combine to solve difficult engineering problems.