Develop and integrate task policies using techniques such as hierarchical planning, reinforcement learning, or hybrid methods.

Design and implement short and long-horizon planning systems for real-world robotic applications.

Prototype and deploy scalable policy architectures that operate under tight latency and compute constraints at the Edge.

Contribute to the evolution of our Edge AI stack by connecting planning with low-level controls and high-level semantic reasoning.

Work with real-world and simulated sensor data to test and refine models; contribute to internal datasets and benchmarking tools.

Participate in project planning, code reviews, and architectural discussions; take ownership of technical areas and deliver high-quality, reliable implementations.

Stay current with trends in AI for robotics (e.g., expert and generalist policies, foundation models), and help integrate relevant advancements into our workflows.

6+ years of experience in AI, robotics, or computer vision.

Bachelor’s degree in a relevant field (e.g., Robotics, Computer Science, Electrical Engineering); M.S. or Ph.D. preferred.

Knowledgeable in the areas of robot locomotion, robot kinematics and robot dynamics.

Proficient in Python, with practical experience using ROS, PyTorch or TensorFlow.

Comfortable working with real-world sensor data (e.g., ToF, vision, IMU) and simulation tools like Gazebo, Isaac Sim, MuJoCo, OpenAI Gym or Unreal.

Experience with DevOps/MLOps tools: Docker, CI/CD pipelines, version control (Git).

Skilled at communicating technical insights, collaborating with multi-disciplinary teams, and contributing to shared architectural decisions.

Experience integrating perception models with control loops, path planning, or robot operating systems (ROS2).

Familiarity with industrial robotics, fixed arm robots, or autonomous systems in structured environments.

Contributions to open-source robotics or AI frameworks.