Co-op: Robotics Perception & Computer Vision

Exol

Wilmington (MA)

On-site

USD 44,000 - 61,000

Full time

14 days+

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Job summary

Symbotic seeks Master’s/PhD students for internship/co‑op roles on the Robot Perception team. You will explore vision-based algorithms, develop perception and SLAM solutions, and contribute to production deployments in a fast-moving robotics environment.

You’ll collaborate across teams, publish results when applicable, and gain hands-on experience with PyTorch/TensorFlow, OpenCV, and cloud-friendly tools while benefiting from relocation support and mentorship.

Qualifications

Pursuing a Master’s or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field.
Proficiency in Python and/or C++, including experience writing code and working in Jupyter notebooks.
Experience with machine learning frameworks such as PyTorch or TensorFlow.
Familiarity with computer vision and perception tools such as OpenCV, including image classification, object detection, segmentation, and camera calibration.
Exposure to robotics perception concepts such as sensor fusion, SLAM, 3D pose representations, coordinate transforms, probabilistic graphical models, and Bayesian inference.
Working knowledge of sensors, including RGB cameras, 3D sensors, and point cloud processing tools such as PCL.
Experience working in Linux environments and using command-line tools.
Familiarity with cloud platforms such as AWS or Azure, REST APIs, and/or SQL-based data tools such as Snowflake.
Exposure to deployment or optimization tools such as Docker, ROS/ROS 2, Jetson, ONNX, VLMs, or Vision-Language-Action models is a plus.

Responsibilities

Research and experiment with traditional computer vision methods and deep learning methods for object detection, classification, segmentation, pose estimation, anomaly detection on images or videos.
Evaluate and benchmark computer vision methods on production data.
Deploy software to production on robot hardware.
Design data pre and post processing methods.
Document designs, code, experiments, evaluations.
Identify high‑impact scenarios where AI Planning and machine learning can be applied to optimize the Symbotic robotics software
Adapt state‑of‑the‑art approaches or develop high‑performance algorithms to solve real‑world multi‑robot problems.
Conduct simulations and experiments to validate the performance of the multi‑robot algorithms.
Present results in verbal and written communications internally, and in top‑tier academic venues.