Staff Specialist Field Engineer, Robotics

Weights & Biases Weights & Biases · Data AI · San Francisco, CA · Monolith COR

Staff Specialist Field Engineer for Robotics at CoreWeave, focusing on deploying AI/ML solutions for robotics customers. The role involves establishing the robotics vertical, defining engagement strategies, leading customer engagements from scoping to deployment, and building/iterating on customer-facing applications using CoreWeave's AI platform. Requires deep understanding of robotics systems, ML capabilities, and the ability to translate field observations into product signals.

What you'd actually do

  1. establish and lead this vertical
  2. 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.
  3. build and iterating on customer-facing applications using the CoreWeave product stack tailored to specific engineering use cases.
  4. gather and synthesise signals from real customer deployments, identifying where customer needs are consistent enough across accounts to inform development of standalone capabilities.
  5. 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.

Skills

Required

  • robotics systems development
  • robot learning
  • AI/ML for physical autonomous systems
  • manipulation
  • locomotion
  • mobile robotics
  • physical system dynamics
  • kinematics
  • engineering constraints
  • Python
  • modern ML frameworks
  • JupyterHub
  • VS Code
  • Marimo
  • W&B Models
  • imitation learning
  • reinforcement learning
  • sim-to-real transfer
  • anomaly detection for physical systems
  • trajectory prediction
  • robot simulation environments (Isaac Sim, MuJoCo, Gazebo, or similar)
  • leading complex technical customer engagements
  • managing multi-stakeholder environments
  • maintaining executive relationships

Nice to have

  • deploying robot learning systems on real hardware
  • managing the sim-to-real gap in production settings
  • foundation models for robotics
  • generalised manipulation
  • generalised locomotion tasks
  • compute-intensive training workloads
  • infrastructure requirements
  • expansion opportunities within strategic customer accounts
  • contributing to internal knowledge frameworks
  • technical publications
  • industry forums in the robotics space
  • ROS/ROS2 and associated tooling

What the JD emphasized

  • 8+ years experience in robotics systems development, robot learning, or AI/ML for physical autonomous systems
  • 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.
  • Able to validate ML solutions on physical grounds and identify when a model output violates the constraints of the real system it represents.
  • Able to translate field observations into structured product signals that are actionable for an engineering team.

Other signals

  • deploying AI solutions within enterprise engineering organisations
  • establish and lead this vertical
  • build and validate ML solutions independently
  • build customer-facing applications tailored to specific robotics use cases
  • translate field observations into structured product signals