Pre-Screening Questionnaire:
– Have you worked in robotics, robot learning, or embodied AI domain?
Must Have Technical/Functional Skills:
PhD in a relevant STEM field, or Master’s with equivalent industry experience in robotics, robot learning, or embodied AI.
Proven experience building and deploying machine learning models on robotic systems—including training, evaluation, and real-world execution or simulation.
Deep understanding of modern AI architectures (e.g., Transformers, diffusion models, VLM/VLAs, CNNs) with strong experience training models at scale.
Strong PyTorch implementation skills, including authoring custom modules, batching, debugging, and performance optimization.
Practical experience with ROS/ROS2 and integrating learned policies into manipulation or motion control workflows.
Demonstrated impact via robot learning publications, open-source contributions, or production robotics deployments.
Roles & Responsibilities:
Design and implement advanced robot learning architectures (e.g., diffusion policies, ACT, VLM/VLA-guided agents, imitation learning) to support dexterous manipulation, path planning, and autonomous task sequencing.
Develop end-to-end policy training pipelines, integrating multi-modal sensory data (RGB, depth, proprioception, force/torque, LiDAR, tactile inputs) with control outputs.
Build policy inference and closed-loop control that connect perception, planning, and execution on physical robotic platforms.
Apply and extend large-scale architectures—LLMs, VLM/VLAs, diffusion models—to embodied tasks, grounding, and sim-to-real adaptation.
Collaborate with cross-functional teams to deploy robot policies on hardware, ensuring robustness, repeatability, and safety.
Lead data strategy for demonstrations, teleoperation, simulation pipelines, and evaluation frameworks for manipulation policies.
Stay current with embodied AI research and share insights internally through discussion, mentorship, and technical presentations
Key Words to search in Resume:
Advanced Robotics – Embodied AI - LLMs, VLM/VLAs, Diffusion models
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