Robotics Algorithm Engineer

Robotics Algorithm Engineer (Perception, Scene Reconstruction & Human-Centric AI)
We are seeking a highly skilled Robotics Algorithm Engineer to join our core R&D team and help build next-generation embodied AI systems. In this role, you will develop cutting-edge algorithms across visual perception, 3D scene understanding, robot learning, and multi-sensor fusion and deploy them directly on real robotic platforms impacting production-level systems.

What Youll Do

• Develop and deploy algorithms for perception, 3D reconstruction, visual SLAM/VIO, and autonomous scene understanding.

• Build robot learning pipelines including imitation learning, reinforcement learning, and foundation model fine-tuning.

• Create data-driven models leveraging large-scale egocentric and human-robot datasets.

• Architect robust multi-sensor fusion frameworks integrating vision, proprioception, and tactile sensing.

• Implement production-level software using ROS/ROS2 , C++, and Python.

• Test, benchmark, and optimize algorithms on physical robotic systems.

• Collaborate cross-functionally on data generation, model evaluation, and system-level optimization.

• Contribute to publications, patents, and internal technical roadmaps.

• Mentor junior engineers and support a culture of innovation.

Minimum Qualifications

• MS or PhD in Robotics, CS, EE, or related field (or equivalent experience).

• 3+ years experience in robotics, computer vision, or ML for embodied systems.

• Strong C++ & Python skills; experience with ROS/ROS2 .

• Solid grounding in 3D geometry, optimization, and estimation.

• Experience in one or more:

  • Visual SLAM / VIO / multi-view geometry

  • 3D reconstruction / mapping

  • Sensor fusion & state estimation

  • Robot learning (IL, RL, or foundation model adaptation)

• Previous deployment of algorithms on real robotic hardware .

Preferred Qualifications

• PhD with research in robot learning, embodied AI, or visual perception.

• Deep knowledge of PyTorch/TensorFlow.

• Experience with imitation learning, reinforcement learning, and policy optimization.

• Experience with CLIP, RT-1, VLA, or similar VLM/robotics foundation models.

• Background in 3D human modeling, MoCap, or human-to-robot demonstrations.

• CUDA, multi-threading, or distributed training experience.

• Publications in ICRA, IROS, CoRL, RSS, CVPR, ICCV, NeurIPS.