Engineer– Embodied AI Lab Automation: Vision-Guided Real Time Robotic Manipulation

Brookhaven National Laboratory•Ridge, NY

3d•$71,900 - $119,000•Onsite

About The Position

Brookhaven National Laboratory’s National Synchrotron Light Source II (NSLS-II) is seeking an engineer to develop an automated protein crystal harvesting system at the Center for BioMolecular Structure (CBMS). This position is a 2-year term, with the possibility of renewal contingent on performance and funding availability. The successful candidate will develop vision-guided robotic manipulation capabilities for detecting, tracking, and harvesting fragile micron-scale protein crystals suspended in liquid droplets. Working closely with a multidisciplinary team of crystallographers, beamline scientists, controls engineers, mechanical engineers, and data scientists, the candidate will help integrate computer vision, machine learning, and robotic manipulation into an automated crystal harvesting platform. This is a hands-on role centered on building and deploying closed-loop perception, visual servoing, and robotic control systems in a real laboratory environment. The position offers the opportunity to advance machine vision, robotic manipulation, and embodied AI techniques while addressing a scientifically impactful problem in structural biology.

Requirements

B.S. in Robotics, Computer Science, Physics, Applied Mathematics, Mechanical Engineering, Electrical Engineering or a related field
Demonstrated experience developing and implementing robotics systems.
Strong programming skills in Python and/or C++
Knowledge of robotic systems: Robot kinematics and coordinate transformations
Motion planning and trajectory generation.
Calibration methods

Nice To Haves

M.S. in Robotics, Computer Science, Physics, Applied Mathematics, Mechanical Engineering, Electrical Engineering or a related field
Experience developing robotic systems that integrate vision-guided control in real-world settings
Experience working with ROS2-based ecosystems
Experience with camera calibration, hand-eye calibration, and coordinate frame registration
Experience integrating machine learning models into robotics
Familiarity with: Micro-Manipulation
Sensor fusion or state estimation
Real-time system debugging and performance tuning

Responsibilities

Develop and implement vision-guided real-time robotic manipulation methods for crystal harvesting
Design and deploy closed-loop perception and control pipelines using visual feedback
Contribute to vision-based servoing approaches for precise, real-time positioning
Integrate perception outputs with motion planning and execution using ROS2 and MoveIt2
Perform system calibration (e.g., hand-eye calibration, camera calibration) to ensure accurate 3D spatial alignment
Evaluate and improve system robustness, including perception uncertainty and failure modes
Collaborate closely with domain experts across crystallography, controls, and engineering
Participate in system testing, tuning, and performance evaluation
Document methods and results; contribute to reports, publications, and presentations