Director of Physical AI Engineering & Data Systems

Scale AI

San Francisco (CA)

On-site

USD 302,400 - 378,000

Full time

14 days+

Get more replies from employers

Send a job-specific resume in minutes.

Benefits offered by this job

Health insurance

Dental & vision coverage

Retirement benefits

Learning stipend

Generous PTO

Commuter stipend

Job summary

Scale AI in San Francisco seeks a Director of Engineering for Physical AI to lead a multi-disciplinary team and own the execution of the Physical AI Data Engine, the platform powering embodied AI deployments.

You will shape the technical vision, guide data collection infra, ML training pipelines, and production systems, collaborating with Operations and GTM to align product direction and scale robotics workloads.

Qualifications

Bachelor's degree in Engineering, Robotics, Computer Science, or a related technical field.
8+ 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.

Responsibilities

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.