物理AI基础设施技术负责人工程师

The Infra-Compute division builds large-scale, highly available cloud and AI infrastructure that powers our public cloud offerings and internal products.

Our US team develops technologies across AI training, inference, and agent infrastructure.

We are expanding this work into Physical AI: intelligent systems that perceive, reason, and act in the physical world.

The team focuses on infrastructure for Physical AI, including data and simulation platforms, distributed training and inference, and deployment on heterogeneous hardware.

We collaborate closely with customers, researchers, open-source communities, and hardware partners to turn emerging research into reliable, scalable systems.

- Track advances in academia, industry, and open-source communities; contribute technical insights, software, and, where appropriate, publications.

- Own the technical strategy and multi-quarter roadmap for major Physical AI infrastructure areas, translating ambiguous research, product, and customer requirements into clear architecture and execution plans.

- Lead architecture and delivery across multiple engineers and partner teams, define system boundaries and interfaces, resolve cross-stack trade-offs, and remain accountable for end-to-end technical outcomes.

- Define interfaces across the Physical AI lifecycle—including data pipelines, simulation, model training, evaluation, inference, and deployment—while directly owning one or more major platform components.

- Mentor engineers, develop technical leaders, and raise the engineering bar across the team.

- Bachelor's degree or above in Computer Science, Computer Engineering, Robotics, Electrical Engineering, Artificial Intelligence, or a related technical field, or equivalent practical experience.

- 5 years of software engineering, machine learning systems, robotics, or related industry experience.

- 3+ years of experience as a tech lead.

- Deep expertise in at least one of the following areas, with working knowledge across one or more of the others: Robotic learning, including imitation learning, reinforcement learning, vision-language-action models, world models, or policy evaluation;

AI training and inference infrastructure, including distributed training, inference engines, GPU kernels, or collective communication;

Robotics data and simulation systems, including multimodal dataset verification, synthetic data generation, simulation, data curation, or sim-to-real workflows.

- Experience taking technically complex systems from prototype through validation and production deployment.

- Strong communication skills, self-motivation, sound engineering judgment, and the ability to work effectively across research and engineering teams.

- Contributions to relevant open-source projects such as Isaac Lab, Isaac Sim, MuJoCo, Cosmos, vLLM-Omni, SGLang Omni, RLInf, etc.

- Experience building large-scale AI or robotics platforms used by multiple teams or external customers, or experience with real-world robot systems and operational challenges.

- Publications at leading machine learning, systems, or robotics conferences.