Interns at Bright Machines will gain hands-on experience in prototyping and validating robotic systems in a team-driven and innovative manufacturing environment.
Prototype and validate robotic systems involving 6-DOF manipulators, end-effectors
Assist with machine learning workflows for visual inspection (defect detection, classification) and vision-based navigation (pose estimation, visual servoing)
Support motion planning, control, and 3D navigation pipelines (path planning, localization, obstacle avoidance)
Design, execute, and document test plans to validate software and hardware performance
Pursuing a BS or MS in Mechanical Engineering, Mechatronics, Robotics, Electrical Engineering, Aerospace Engineering, Computer Science, or a related field
Experience with programming and applied math (e.g., Python, C++, Matlab)
Coursework or project experience in robotics, controls, or machine learning
Strong communication skills and attention to detail
Examples of projects showing your problem-solving approach
Experience with motion planning and controls (e.g., ROS/ROS2, MoveIt)
Experience with deep learning frameworks (e.g., PyTorch) for vision or navigation tasks
Exposure to 3D navigation, SLAM, visual odometry, or sensor fusion
Experience programming robots.
Design team experience. (for example: Formula SAE, robotics clubs, etc.)
Mechanical design/build experience for fixtures.
Experience with metrology, kinematics, and precision mechanisms.
You are customer obsessed
You believe in the power of a team, diversity of thought, and unity of action
You always act with integrity, keep your commitments, and practice transparency
You think boldly, lead with courage, and demand the highest standards
You drive for results, and act like an owner
Bright Machines is an innovative manufacturer specializing in AI-powered robotics to streamline the assembly of data center infrastructure. We cater to the needs of hyperscalers and OEMs, leveraging advanced technology to efficiently build vital hardware that fulfills the growing demand for computational power.
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