Co-op: Perception R&D

Exol•Wilmington, MA

13d•Hybrid

About The Position

Symbotic is seeking a Co-Op to join our Perception R&D team. This role will assist in developing and prototyping perception solutions and machine learning models that enable our robots to perform effectively and autonomously in complex warehouse environments. This hands-on role provides a unique opportunity to contribute to the integration of perception algorithms, sensors, and robotics within a state-of-the-art automation platform.

Requirements

Currently pursuing a Master’s degree in Computer Science, Robotics, Electrical Engineering, or a related field.
Proficiency in Python and C++.
Familiarity with computer vision or perception frameworks such as OpenCV.
Exposure to RGB cameras, 3D sensors, or point cloud processing tools (e.g., PCL).
Experience with containerization tools such as Docker.
Knowledge of Robot Operating System (ROS) and ROS 2.
Familiarity with Linux-based Systems and Real-Time Operating Systems.
Required proficiency in PyTorch and/or TensorFlow.

Nice To Haves

Experience with robots, specifically AGVs.
Exposure to edge computing and firmware development.
Experience with different perception sensors (e.g., LiDAR, camera, radar, laser range sensor, IR time-of-flight sensors).
Familiarity with robotics simulation tools and environments (e.g., Gazebo).

Responsibilities

Assist in Developing Perception Solutions: Help design and prototype perception algorithms for robotic systems, with applications in object detection, segmentation, and pose estimation.
Data Pre- and Post-Processing: Develop and refine data pre-processing and post-processing methods to enhance image and point cloud processing workflows.
Benchmark and Deploy: Evaluate perception models on production data and deploy optimized solutions on robotic hardware.
Collaborate in Cross-Functional Teams: Work closely with software and hardware engineers to integrate sensors such as LiDAR, cameras, and other perception sensors.
Document and Communicate: Maintain detailed documentation for code, designs, and experimental results.