ML Engineer – Robotics

Responsibilities

• Develop and optimize ML models for perception, motion planning, and control.
• Build computer vision and sensor-fusion systems using camera, LiDAR, and IMU data.
• Integrate learning-based models with robotics software stacks (ROS/ROS2).
• Design pipelines for data collection, simulation, and reinforcement learning.
• Collaborate with robotics and hardware engineers to deploy models in live environments.
• Continuously evaluate model performance and robustness across diverse scenarios.

• Proficiency in Python and C++, with hands‑on experience using PyTorch and/or Tensor Flow to build and deploy models.
• Experience with ROS or ROS2 and integrating ML models into robotics software stacks for live deployments.
• 3–10 years of experience in machine learning, robotics, or computer vision.
• Strong grasp of robotics concepts such as localization, SLAM, control systems, and sensor fusion.
• Experience with simulation environments (e.g., Gazebo, Isaac Sim, CARLA, Mu Jo Co , or PyBullet) for training, testing, or validation.
• Familiarity with reinforcement learning, imitation learning, or adaptive control techniques applicable to robotics.
• Experience deploying ML models in real‑time or embedded environments.
• Nice to Have:
  Experience with on‑board/edge deployment and optimizing models for constrained hardware.

Demonstrates expertise in developing and optimizing machine learning models for robotics applications, with a strong focus on computer vision, sensor fusion, and real‑time deployment. Proficient in integrating learning‑based models with robotics software stacks and evaluating model performance in diverse environments.