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We are seeking a Lead Applied Scientist to spearhead the creation of sophisticated robot learning policies that enable dexterous manipulation in practical settings.
You will guide the end-to-end process of designing, training, and deploying models that fuse multimodal perception, policy formulation, and robotic execution. Join our collaborative AI Research team to advance embodied AI and tackle industrial-scale autonomy and simulation challenges. Apply now to become a key player in pushing the frontier of robotics.
Responsibilities
- 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
Requirements
- 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
Nice to have
- 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