Robotics Engineer, Manipulation
$100k - $300k • San Mateo, CA
Posted 4mo ago
Job Location
San Mateo, CA
Tech Stack
Remote Work Policy
On-site
Categories
Machine Learning Engineer
About the job
Skild AI is developing the world's first general-purpose robotic intelligence capable of robust adaptation to unseen scenarios. We are seeking a versatile Robotics Engineer to develop and implement software solutions for our manipulation systems, focusing on deploying state-of-the-art learning-based algorithms on real robot setups for daily manipulation tasks. This role involves close collaboration with researchers and the ML team, requiring comfort with building high-quality software for robotic manipulation and deploying it on robot hardware.
Responsibilities
- Design algorithms, models, and techniques for robotic manipulation tasks.
- Design hardware and software systems for robotic manipulation tasks.
- Write controllers and perception stacks for real-world robotic deployment.
- Write and maintain production-level C++ and Python code for robotic manipulation platforms.
- Collaborate with machine learning engineers to deploy state-of-the-art models on robots.
- Continuously improve and optimize robotic software for performance, reliability, and scalability.
Requirements
- BS, MS or higher degree in Computer Science, Robotics, Engineering or a related field, or equivalent practical experience.
- Experience in designing robotic manipulation systems and deploying software on real robots.
- Experience with writing controllers and knowledge of robotics (e.g., kinematics, dynamics, control, motion planning, SLAM).
- Proficiency in Python and at least one deep learning library (e.g., PyTorch, TensorFlow, JAX).
- Systems-level understanding of software modules and interfaces in robotic applications (ROS/ROS2, simulators).
- Publications at top-tier ML, robotics or CV conferences (e.g., NeurIPS, ICML, ICLR, CoRL, RSS, ICRA, CVPR, ECCV, ICCV).
- Experience with designing custom robot hardware solutions.
- Proficiency with various robot learning techniques (RL, imitation learning).
- Experience working with modern computer vision algorithms, sensors (LiDAR, camera, radar), 3D data (meshes, point clouds), segmentation, tracking, and object detection.