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Staff Specialist Field Engineer, Robotics

Job in Sunnyvale, Santa Clara County, California, 94087, USA
Listing for: Socket.dev
Full Time position
Listed on 2026-08-03
Job specializations:
  • Software Development
    Robotics, AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 177000 - 237000 USD Yearly USD 177000.00 237000.00 YEAR
Job Description & How to Apply Below

Core Weave is The Essential Cloud for AI™. Built for pioneers by pioneers, Core Weave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, Core Weave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, Core Weave became a publicly traded company (Nasdaq: CRWV) in March 2025.

Learn more at

What You’ll Do

The Physical AI Field Engineering team at Core Weave deploys AI solutions within enterprise engineering organisations, helping customers realise measurable value from machine learning applications. As we build our presence in Robotics, we are seeking a Staff Specialist Field Engineer to establish and lead this vertical. You will define how the team engages with robotics customers, set the technical standard for the practice, and lead complex customer engagements from initial scoping through to embedded deployment and expansion.

This role requires genuine depth on both sides of the engagement: you must be credible to the control engineers, roboticists, and ML researchers in the room, and capable of building and validating ML solutions independently using our product stack. You will work across Core Weave’s AI platform (including Monolith, Marimo, and W&B Models) to design and deploy solutions and build customer-facing applications tailored to specific robotics use cases.

A core part of this role is building and iterating on customer-facing applications using the Core Weave product stack tailored to specific engineering use cases. These applications are the product the customer uses; you are not just deploying tools but building at the product edge. You will gather and synthesise signals from real customer deployments, identifying where customer needs are consistent enough across accounts to inform development of standalone capabilities.

You will contribute to the wider Field Engineering team’s Physical AI knowledge framework and provide critical signals of what customers need in the field and what ultimately gets built in the product.

Who You Are
  • 8+ years experience in robotics systems development, robot learning, or AI/ML for physical autonomous systems, with experience in manipulation, locomotion, or mobile robotics.
  • Deep understanding of physical system dynamics, kinematics, and the engineering constraints that govern real-world robot behaviour.
  • Hands-on ML capability: able to build, validate, and deploy ML solutions independently in Python using modern ML frameworks.
  • Proficient in working directly in Jupyter Hub, VS Code, Marimo, and W&B Models to build and deploy customer-facing applications.
  • Working knowledge of ML approaches relevant to robotics: imitation learning, reinforcement learning, sim-to-real transfer, anomaly detection for physical systems, or trajectory prediction.
  • Able to validate ML solutions on physical grounds and identify when a model output violates the constraints of the real system it represents.
  • Proven experience leading complex technical customer engagements, managing multi-stakeholder environments, and maintaining executive relationships.
  • Familiarity with robot simulation environments (Isaac Sim, Mu Jo Co , Gazebo, or similar) and their role in training and validating robot learning pipelines.
  • Able to translate field observations into structured product signals that are actionable for an engineering team.
  • Bachelor’s or Master’s degree in Robotics, Mechanical Engineering, Electrical Engineering, Computer Science, Physics, or a related technical discipline.
Preferred
  • Experience deploying robot learning systems on real hardware, including managing the sim-to-real gap in production settings.
  • Familiarity with foundation models for robotics and their application to generalised manipulation or locomotion tasks.
  • Experience working with compute-intensive training workloads and an understanding of the infrastructure requirements they create.
  • Experience identifying and progressing expansion opportunities within strategic customer accounts.
  • Track record of contributing to…
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