Staff AI/ML Engineer - AMR Robot Perception

Overview

On Site
Hybrid
Full Time

Skills

Artificial Intelligence
Reporting
Marketing Intelligence
MI
Algorithms
Real-time
Collaboration
System Integration
Cloud Computing
Training
Evaluation
Computer Science
Electrical Engineering
Python
C++
Machine Learning (ML)
PyTorch
TensorFlow
Sensors
Fusion
Data Processing
Robotics
Internationalization And Localization
Mapping
ROS
Testing
Computer Hardware
Hardware-in-the-loop Simulation
Continuous Integration
Continuous Delivery
Software Development
AV
Audiovisual
FOCUS
SAP BASIS
Recruiting
Screening

Job Details

Job Description

Hybrid: This role is categorized as hybrid. This means the successful candidate is expected to report to the Cole Technical Center in the Warren MI three times per week, at minimum.

Role: We are seeking a highly skilled Staff Robot Perception Engineer to lead the development of cutting-edge machine learning systems that enable perception capabilities for autonomous robots. You will be instrumental in designing, training, and deploying camera- and LiDAR-based models for object detection, classification, tracking, and segmentation in complex, real-world indoor environments.

This role spans the full model development lifecycle and involves close collaboration with cross-functional teams, including hardware, localization, and planning. You will also help build scalable offline tools for data processing, training, evaluation and validation.

About the Organization

The Robotics Software team is pioneering the next generation of autonomous robotic systems, focusing on autonomous mobile robots (AMRs) and intelligent robotic platforms. We develop full-stack robotics capabilities-from perception and planning to control and system integration-bringing innovative, real-world autonomous solutions to the future of the work.

What You'll Do:

  • Design, implement, and deploy perception algorithms and models for object detection, tracking, classification, and segmentation using camera and LiDAR data


  • Develop and apply sensor fusion techniques to improve accuracy and robustness


  • Develop offline tools to evaluate and benchmark the performance


  • Optimize perception models for real-time performance and hardware constraints


  • Collaborate with motion planning and localization teams to ensure seamless system integration


  • Design and maintain cloud-based pipelines for data preprocessing, model training, and evaluation


  • Contribute to system-level testing and validation in real-world scenarios


Additional Job Description

Your Skills & Abilities:

  • Bachelor's, Master's, or Ph.D. in Robotics, Computer Science, Electrical Engineering, or a related field


  • 3+ years of experience developing and deploying real-world robot perception systems


  • Strong programming skills in Python and C++


  • Hands-on experience with machine learning frameworks such as PyTorch or TensorFlow


  • Expertise in multi-modal sensor fusion and data processing


  • Solid understanding of robotics systems including localization, mapping, and planning


What Will Give You A Competitive Edge:

  • Experience with ROS/ROS2 and Navigation2 framework


  • Familiarity with testing frameworks and hardware-in-the-loop (HIL) testing


  • Understanding of CI/CD pipelines and modern software development practices


  • Background in autonomous vehicles (AV), ADAS, or industrial automation


Relocation: This job may be eligible for relocation benefits.

#LI-MH2

About GM

Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.

Why Join Us

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Benefits Overview

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We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire .

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