Research, design, and develop advanced robot learning policies for autonomous robotic systems.
Work on modern robotic intelligence architectures such as: - Vision Language Action (VLA) Models
- World Action Models
- Transformer-based policy learning frameworks
Build, configure, and maintain robotic simulation environments using: - Gazebo
- MuJoCo
- NVIDIA Isaac Sim & Isaac Lab
Design and implement training pipelines using: - Reinforcement Learning (RL)
- Imitation Learning (IL)
- Hybrid policy optimization techniques
Train, fine-tune, and evaluate robotic policies including: - Action Chunking Transformer (ACT)
- SmolVLA
- JEPA-derived robotic policies
Analyse policy performance and optimize model efficiency for improved robotic task execution.
Contribute to the development of next-generation embodied AI and autonomous robotics solutions.
Collaborate with cross-functional R&D teams to deploy production-ready robotic systems.
Conduct experimentation, testing, and validation for real-world robotics applications.
Requirements
- B.E. / B.Tech / M.E. / M.Tech / Master’s degree in:
- Robotics
- Computer Science
- AI
- Mechatronics
- Electronics
- or related fields
- Minimum 2+ years of experience in robotics software research or related domain.
- Strong understanding of:
- Robot learning architectures
- Autonomous robotic systems
- Embodied AI concepts
- Hands-on experience with:
- VLA Architectures
- World Action Models
- Transformer Policies
- Behaviour Cloning
- Policy Optimization
- Experience working with simulation platforms such as:
- Gazebo
- MuJoCo
- NVIDIA Isaac Sim
- Isaac Lab
- Practical exposure to:
- Reinforcement Learning
- Imitation Learning
- ML/AI model training
- Strong research and experimental problem-solving mindset.
- Ability to convert research ideas into deployable robotic solutions.
- Good communication and collaboration skills.
- Passion for robotics, autonomous systems, and advanced AI technologies.