ArcBest
Job Description
The Machine Vision Engineering Fellow serves as the technical authority and visionary for all machine vision initiatives within the Vaux division. This position leads the design, development, and deployment of advanced perception systems that power Vaux Vision, Smart Autonomy, and future innovations. The work of the Machine Vision Engineering Fellow directly impacts the efficiency, safety, and intelligence of next-generation material handling systems across warehouses, distribution centers, and industrial environments.
Responsibilities
Collaborate cross-functionally with robotics, embedded systems, cloud, and product teams to integrate vision systems into the Vaux OS and Smart Autonomy platforms.
Represent ArcBest in industry forums, patent filings, and academic collaborations.
Architect and lead the development of real-time 3D vision systems for freight measurement, object detection, and autonomous navigation.
Drive innovation in AI/ML-based perception, including damage detection, freight classification, and anomaly detection.
Mentor and guide senior engineers and technical leads across multiple teams.
Other duties and projects, as assigned.
Design and optimize multi-sensor fusion algorithms (e.g., LiDAR, stereo/depth cameras, IMUs) for forklift-mounted and mobile robotic platforms.
Requirements
Education:
Master's Degree in Computer Vision, Robotics, Electrical Engineering, or related field, required
PhD is a plus.
Experience:
15+ years of experience in machine vision.
5 years of experience in autonomous systems or industrial automation.
Proven track record of delivering production-grade vision systems in robotics, logistics, or manufacturing.
Computer Skills:
3D perception (e.g., SLAM, point cloud processing, depth estimation), required
Real-time image processing and embedded vision, required
Sensor calibration and synchronization, required
AI/ML for visual inspection and classification, required
Proficiency in C++, Python, ROS, OpenCV, and deep learning frameworks (e.g., TensorFlow, PyTorch), required
Experience with cloud-connected vision systems and edge AI deployment, required
Additional Requirements:
Experience with forklift or AMR-mounted vision systems, preferred
Familiarity with NTEP certification and industrial safety standards, preferred
Contributions to open-source vision libraries or published research in top-tier conferences, preferred
Other Details
Work Hours:
Generally, 8:00 am - 5:00 pm with occasional irregular hours depending on workload.
Travel Requirements:
Occasional (25%-50%)
Compensation:
This is a salary position paid biweekly.
Pay Range: $200,000.00-$225,000.00 base salary.
About Us
ArcBest Technologies, ArcBest's tech and innovation team, provides custom-built solutions and leading-edge technologies that help our customers successfully navigate the complex supply chain landscape. The team is made up of highly engaged, creative tech and analytics professionals who create smart solutions that help keep the global supply chain moving.
An Equal Opportunity Employer M/F/Vet/Disability
The Machine Vision Engineering Fellow serves as the technical authority and visionary for all machine vision initiatives within the Vaux division. This position leads the design, development, and deployment of advanced perception systems that power Vaux Vision, Smart Autonomy, and future innovations. The work of the Machine Vision Engineering Fellow directly impacts the efficiency, safety, and intelligence of next-generation material handling systems across warehouses, distribution centers, and industrial environments.
Responsibilities
Collaborate cross-functionally with robotics, embedded systems, cloud, and product teams to integrate vision systems into the Vaux OS and Smart Autonomy platforms.
Represent ArcBest in industry forums, patent filings, and academic collaborations.
Architect and lead the development of real-time 3D vision systems for freight measurement, object detection, and autonomous navigation.
Drive innovation in AI/ML-based perception, including damage detection, freight classification, and anomaly detection.
Mentor and guide senior engineers and technical leads across multiple teams.
Other duties and projects, as assigned.
Design and optimize multi-sensor fusion algorithms (e.g., LiDAR, stereo/depth cameras, IMUs) for forklift-mounted and mobile robotic platforms.
Requirements
Education:
Master's Degree in Computer Vision, Robotics, Electrical Engineering, or related field, required
PhD is a plus.
Experience:
15+ years of experience in machine vision.
5 years of experience in autonomous systems or industrial automation.
Proven track record of delivering production-grade vision systems in robotics, logistics, or manufacturing.
Computer Skills:
3D perception (e.g., SLAM, point cloud processing, depth estimation), required
Real-time image processing and embedded vision, required
Sensor calibration and synchronization, required
AI/ML for visual inspection and classification, required
Proficiency in C++, Python, ROS, OpenCV, and deep learning frameworks (e.g., TensorFlow, PyTorch), required
Experience with cloud-connected vision systems and edge AI deployment, required
Additional Requirements:
Experience with forklift or AMR-mounted vision systems, preferred
Familiarity with NTEP certification and industrial safety standards, preferred
Contributions to open-source vision libraries or published research in top-tier conferences, preferred
Other Details
Work Hours:
Generally, 8:00 am - 5:00 pm with occasional irregular hours depending on workload.
Travel Requirements:
Occasional (25%-50%)
Compensation:
This is a salary position paid biweekly.
Pay Range: $200,000.00-$225,000.00 base salary.
About Us
ArcBest Technologies, ArcBest's tech and innovation team, provides custom-built solutions and leading-edge technologies that help our customers successfully navigate the complex supply chain landscape. The team is made up of highly engaged, creative tech and analytics professionals who create smart solutions that help keep the global supply chain moving.
An Equal Opportunity Employer M/F/Vet/Disability