Research Robotics

We are hiring for a Research Robotics Engineer role with a fast-growing AI company building the intelligence layer for physical-world AI. The company is working at the intersection of robotics perception, embodied AI, egocentric video understanding, and voice-driven interaction to develop AI systems that can perceive, reason, and act in real-world environments.

💡 Why Join?

Work on challenging robotics and AI problems involving 3D perception, computer vision, multimodal learning, sensor data, and embodied intelligence. You'll collaborate with a strong research and engineering team to develop systems that operate in complex, real-world environments, work on cutting-edge research, and contribute directly to building the next generation of intelligent physical-world systems.

Roles and Responsibilities:

• 2+ years on problems in this space.
• Publications in egocentric vision, 3D hand pose, VLA / VLM models, or robot learning.
• MS / PhD in computer vision, robotics or ML — or equivalent published research output.
• Egocentric pose that survives the camera. We have narrowed the suspects (rectification residual, FOV, shutter type) and currently route between several public SLAM / VIO systems per clip.
• Metric 3D hand pose. Build the in-house hand model that is stable in the world frame, and decide the representation (MANO topology, 6D vs axis-angle) rather than inheriting it.
• Deep hands-on work in at least two of: 3D hand / human pose reconstruction, stereo or monocular depth, VIO / SLAM, open-vocabulary detection and segmentation, video VLMs.
• You have trained a perception model that beat an off-the-shelf baseline on a real domain and shipped it. Data curation, loss design and eval decisions were yours.
• Multi-view geometry is fluent, not looked up: rectification, epipolar residual, Q matrix and depth sign conventions, triangulation, world vs camera frame.
• You can read a calibration report and say whether the problem is the camera or the model — and be right.
• Strong Python and PyTorch; you own training and inference code, not notebooks.
• Production inference experience on AWS: containerization, batch pipelines, orchestration.

Location: Bangalore Onsite