The Role
Research, design, and implement robot learning policies and training pipelines (RL, IL, hybrid) using transformer-based and VLA architectures; build and maintain simulations (Gazebo, MuJoCo, Isaac); train, evaluate, and optimize policies for real-world autonomous robotic systems; collaborate with R&D to deploy production-ready embodied AI solutions.
Summary Generated by Built In
- Vision Language Action (VLA) Models
- World Action Models
- Transformer-based policy learning frameworks
- Gazebo
- MuJoCo
- NVIDIA Isaac Sim & Isaac Lab
- Reinforcement Learning (RL)
- Imitation Learning (IL)
- Hybrid policy optimization techniques
- Action Chunking Transformer (ACT)
- SmolVLA
- JEPA-derived robotic policies
Requirements
- B.E. / B.Tech / M.E. / M.Tech / Master’s degree in:
- Robotics
- Computer Science
- AI
- Mechatronics
- Electronics
- or related fields
- Robotics
- Minimum 2+ years of experience in robotics software research or related domain.
- Strong understanding of:
- Robot learning architectures
- Autonomous robotic systems
- Embodied AI concepts
- Robot learning architectures
- Hands-on experience with:
- VLA Architectures
- World Action Models
- Transformer Policies
- Behaviour Cloning
- Policy Optimization
- VLA Architectures
- Experience working with simulation platforms such as:
- Gazebo
- MuJoCo
- NVIDIA Isaac Sim
- Isaac Lab
- Gazebo
- Practical exposure to:
- Reinforcement Learning
- Imitation Learning
- ML/AI model training
- Reinforcement Learning
- 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.
Skills Required
- 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 and autonomous robotic systems
- Hands-on experience with VLA architectures, World Action Models, and transformer policies
- Experience with behaviour cloning and policy optimization techniques
- Experience working with Gazebo, MuJoCo, NVIDIA Isaac Sim, and Isaac Lab
- Practical exposure to Reinforcement Learning, Imitation Learning, and ML/AI model training
- Experience training and evaluating policies such as ACT, SmolVLA, or JEPA-derived policies
- 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
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The Company
What We Do
Marketscope is a technology company specializing in the development and integration of Advanced Driver Assistance Systems (ADAS) and the scaling of production-grade AI/ML applications. The company focuses on AI platform engineering and product stacks, targeting strategic enterprise accounts and government sales, particularly within the Indian market, while expanding its reach into new international industries.
