Physical Intelligence

Build and operate the distributed data infrastructure that powers large-scale robot learning, from multimodal data ingestion through training and evaluation. This systems-focused role combines data engineering, storage, and machine learning infrastructure at petabyte scale.

Build and scale the infrastructure powering Physical Intelligence’s large-scale model training, from GPU/TPU orchestration to efficient JAX pipelines. You’ll partner closely with researchers to turn new ideas into reliable, production-grade training runs.

Build and own the quality assurance systems that validate multimodal data before it is used to train robot foundation models. You will combine hands-on software development, data auditing, vendor quality management, and measurable quality standards.

Build full-stack internal products that help Physical Intelligence’s research and operations teams move faster, from annotation tooling and workflow systems to dashboards and production services. This role combines hands-on software engineering with product ownership in a robotics and AI research environment.

Build the software platform that powers reliable, technician-friendly manufacturing and production testing for Physical Intelligence’s robotic systems. You’ll connect local hardware stations, hosted workflows, firmware, inventory systems, and test infrastructure as production volume grows.

Build dependable, low-latency software for deploying robots in real-world customer environments. You’ll develop teleoperation interfaces, optimize real-time video and network systems, and improve performance and reliability across the robotics stack.

Own the build, release, and developer infrastructure that enables robotics engineers to ship reliably at scale. This senior hands-on role focuses on CI/CD, Bazel build systems, Kubernetes, cloud infrastructure, and developer productivity.

Build the low-latency runtime systems that make Physical Intelligence’s robots, sensors, and control systems operate reliably in real time. This role focuses on Linux, performance optimization, streaming, drivers, and hardware interfaces rather than ML model design.

Build and scale the infrastructure that powers large-scale model training, from distributed GPU/TPU orchestration to efficient JAX pipelines. You’ll work closely with researchers to turn experiments into reliable, production-grade training runs.