Design and deploy cutting-edge robot learning models and policy architectures for manipulation and autonomous task sequencing, incorporating multimodal sensory inputs, perception, planning, and execution on robotic platforms with ROS/ROS2

Develop and refine comprehensive policy training pipelines, including policy inference and closed-loop control systems, tailored for both real and simulated settings

Lead the data strategy for demonstrations, teleoperation, simulation workflows, and evaluation frameworks to facilitate reliable, safe, and repeatable robot policy implementations

Collaborate with multidisciplinary teams and AI infrastructure specialists to launch scalable, production-grade robotics solutions

Monitor the latest embodied AI research, contribute to reproducible studies, and disseminate knowledge via mentorship and technical talks

Enhance PyTorch implementations through custom module development and performance optimization to ensure efficient model deployment

Doctorate in a relevant STEM discipline or Master’s degree with equivalent industry experience in robotics or embodied AI

Minimum of 7 years experience developing and deploying machine learning models on robotic platforms, integrating multimodal sensory data with control outputs

In-depth understanding of contemporary AI architectures such as Transformers, diffusion models, and VLM/VLAs

Proficient in PyTorch with experience creating custom modules and optimizing model performance

Hands-on experience with Robot Operating System (ROS/ROS2) and integrating policies within robotic systems

Demonstrated impact through scholarly publications, open-source projects, or production system deployments

Capability to lead intricate projects involving collaboration across various teams

Excellent problem-solving, analytical abilities, and communication skills with English proficiency at C2 level

Expertise in computer vision related to 3D data, including perception and affordance modeling

Knowledge of simulation tools like Isaac Sim, Mujoco, Gazebo, or PyBullet

Proficiency in dexterous manipulation and executing multi-step autonomous tasks

Awareness of visual trends and their application to embodied AI challenges

Experience in customizing foundation models for embodied control and instruction-following agents