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Bengaluru location and large employer increase applicant density, but specialized robotics skills limit competition.
Robotics, sim-to-real, and embedded inference expertise is industry-specific and not easily transferable.
Senior lead role requiring ROS2, simulation, C++, Python, and robot-learning expertise narrows candidate pool.
Own the entire technical architecture for Physical AI including perception, policy, planning, control, and data layers.
Lead sim-to-real pipelines with simulation, digital twins, and domain randomization to train and validate robotics policies at scale.
Manage client engagements to translate operational problems into robotics solutions and lead multidisciplinary teams of engineers and scientists.
Strong hands-on expertise in AI/ML and robotics, including experience with imitation learning, policy learning, or vision-language-action (VLA) models.
Proficiency in Python and C++ for real-time robotics control loops and experience with ROS 2 and simulators like Isaac Sim, MuJoCo, Gazebo, or Unity.
Demonstrated track record of taking robotics systems from prototype to reliable deployment in real environments.
Experience leading technical architecture decisions, mentoring engineering teams, and interacting with customers and executives.
Experienced technical leader capable of defining and defending engineering standards in Physical AI and robotics.
Comfortable operating between research and production, including client-facing roles involving proof-of-value deployment.
Familiarity with robot learning, simulation-to-real-world transfer, and managing cross-disciplinary teams in embedded AI robotics contexts.