The opportunity
The Director of Engineering will report to the General Manager of Physical AI, and will be responsible for leading a multi-disciplinary engineering organization. In this senior leadership role, you will own the execution of the Physical AI Data Engine — the platform powering the…
What you'll do
Set and drive the technical vision across data collection infrastructure,: teleoperation systems, ML training pipelines, model evaluation frameworks, annotation tooling, and research
Lead a multidisciplinary engineering organization—spanning engineering: managers, software engineers, ML engineers, and ML research scientists—while designing the organizational structure, talent strategy, and culture required to scale rapidly without compromising on quality or strategic alignment
Maintain exceptional technical and operational excellence by deeply: understanding team deliverables, asking incisive questions, identifying slipping standards early, and knowing precisely when to step in
Drive cross-functional alignment across Engineering, Operations, and GTM on: platform architecture, release processes, and shared priorities
Collaborate with researchers and clients to architect and deliver scalable,: production-grade data infrastructure tailored for complex robotics workloads
Bachelor's degree in Engineering, Robotics, Computer Science, or a related technical field
What they're looking for
- + years of engineering experience in fast-paced environments, including 4+: years direct people management demonstrated history of recruiting, mentoring, and developing high-performing technical teams through rapid growth and change
- Experience leading technical execution for complex hardware-software systems,: with deep domain knowledge in Robotics, Autonomous Vehicles, Computer Vision, and/or Machine Learning strongly preferred
- Deep fluency in the ML development lifecycle: training pipelines, data flywheels, and evaluation frameworks as systems you've built and owned
- Comfortable leading teams across Python, C++, and TypeScript/Node stacks,: distributed systems, cloud infrastructure (AWS, Kubernetes), and workflow orchestration (Temporal, Airflow)
