Develop, implement, and deploy cutting-edge robot learning frameworks and policy architectures for manipulation and autonomous task sequencing that integrate multimodal sensory inputs, perception, planning, and execution using ROS/ROS2

Build and refine comprehensive policy training pipelines, encompassing policy inference and closed-loop control for both simulated and real-world applications

Direct data management strategies for demonstrations, teleoperation, simulation workflows, and evaluation systems to guarantee reliable and safe deployment of robot policies

Partner with cross-disciplinary teams and AI infrastructure specialists to deliver scalable, production-level robotics solutions

Keep abreast of advancements in embodied AI research, contribute to reproducible studies, and disseminate knowledge through mentorship and technical talks

Enhance PyTorch implementations by creating custom modules and optimizing for peak performance and efficient deployment

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

Minimum 5 years of hands-on experience designing and deploying machine learning models on robotic platforms, including integration of multimodal sensory signals and control outputs

Comprehensive understanding of contemporary AI architectures such as Transformers and diffusion models

Advanced proficiency with PyTorch, including developing custom components and performance optimization

Practical expertise with Robot Operating System (ROS/ROS2) and integrating policies into robotic control systems

Demonstrated contributions through publications, open-source projects, or operational deployments

Capability to manage complex projects involving interdisciplinary collaboration

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

Experience working with 3D computer vision, including perception and affordance modeling

Familiarity with simulation environments like Isaac Sim, Mujoco, Gazebo, or PyBullet

Expertise in dexterous manipulation and executing multi-step autonomous tasks

Awareness of visual trends and their relevance to embodied AI applications

Experience adapting foundation models for embodied control and agents following instructions