Physical Intelligence
AI Frontier · Robotics foundation models (PI)
AI Frontier · Robotics foundation models (PI)
Currently tracking 7 active AI roles, up 17% versus the prior 4 weeks. Primary focus: Data · Engineering. Salary range $52k.
Physical Intelligence currently has 7 active AI-related job listings. The majority of these roles, 71%, are focused on data. Engineering is the dominant function, with 5 positions. The company is hiring for roles that frequently involve embodied_ai, multimodal, and vision technologies.
Physical Intelligence currently has 8 active AI-related roles in our index. The most common open titles are: Applied Researcher, Controls Engineer, Deployments Software Engineer, ML Infra Engineer, ML Infra Engineer (TPU/Jax/Optimization). Most positions are in Engineering and Research.
Physical Intelligence's active AI hiring is concentrated in: data (63%), agents (25%), application (13%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Physical Intelligence is hiring AI talent in: United States (8 roles).
Job postings at Physical Intelligence most frequently reference: embodied ai, model serving, multimodal, rl robotics, inference infra.
In the past 30 days, Physical Intelligence has posted 2 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Applied Researcher This role focuses on deploying and debugging learned policies on physical robots, involving training and tuning policies, writing production-quality code, and debugging the full stack. It bridges research and operations by translating research advances into deployable systems and gathering feedback from real-world failures. The role requires hands-on experience with robot deployment, strong engineering skills, and a practical, debugging-oriented mindset. | AgentPost-train | 9 |
| ML Infra Engineer (Supercomputing) ML Infra Engineer responsible for designing and building a scheduling and compute layer for large-scale AI model training across heterogeneous GPU/TPU clusters. This role focuses on intelligent resource allocation, utilization, fault tolerance, and making distributed training seamless, extending to inference and robot deployment. | ServeAgent | 8 |
| Robot Operator This role involves teleoperating robotic arms to collect high-quality demonstration data for training foundation models for physical world AI. The operator will perform various tasks, maintain data quality, and meet collection metrics. It's a hands-on, metrics-driven position focused on generating training data for AI-powered robots. | Data | 8 |
| ML Infra Engineer (TPU/Jax/Optimization) ML Infra Engineer focused on scaling and optimizing training systems and core model code, managing GPU/TPU compute, job orchestration, and building efficient JAX training pipelines. Collaborates with researchers to translate ideas into production training runs. | Data | 8 |
| ML Infra Engineer ML Infra Engineer to scale and optimize training systems and core model code, managing GPU/TPU compute, job orchestration, and JAX training pipelines. Collaborates with researchers to translate ideas into production training runs. | Data | 8 |
| Software Engineer (AI Productivity) Software Engineer focused on AI productivity, building and rolling out tools to help AI agents, assistants, integrations, and automation be useful across the company. This role involves partnering with various teams to understand workflows, build suitable tools, and drive adoption, while also focusing on security, data access, and measuring impact. | Agent | 7 |
| Deployments Software Engineer This role focuses on deploying AI-powered robots into the physical world by building and optimizing the software systems that enable teleoperation, real-time performance, and reliability. It involves integrating foundation models with robot hardware, designing user interfaces for control, and ensuring the system is robust enough for customer operations. | AgentServe | 7 |
| Controls Engineer Controls Engineer for Physical Intelligence, designing and implementing algorithms for robot control, including PID, LQR, MPC, and neural-network-driven control. Responsibilities include building and validating models, developing real-time loops, owning robotic bring-up, debugging complex systems, and working with sensor/actuator subsystems. Requires strong understanding of model-based control, Python/C++ proficiency, and real-time loop tuning skills. Bonus for robot learning or manipulation experience. | ShipServe | 7 |