Machine Learning Engineer

The ASUS Robotics & AI Center is seeking a Machine Learning Engineer to join our global research and development team. This role focuses on designing, implementing, and optimizing computer vision and perception systems that power our next-generation autonomous platforms.

We are looking for a hands-on engineer with experience deploying machine learning models into production, a strong foundation in computer vision and digital imaging, and a passion for translating algorithms into real-world solutions. The ideal candidate thrives in a multidisciplinary environment, contributing to robust, real-time perception pipelines that support advanced AI and robotics applications.

Roles & responsibilities

• Develop and deploy machine learning models for computer vision and object recognition tasks.

• Optimize models for real-time performance on embedded and edge computing platforms.

• Build and maintain perception pipelines that integrate data from cameras and other sensors.

• Collaborate with cross-functional teams, including robotics, systems, and software engineers, to deliver production-ready solutions.

• Evaluate and implement state-of-the-art techniques in deep learning, object detection, and visual tracking.

• Design and execute experiments, including simulation and real-world field testing, to validate model performance.

• Maintain and improve datasets, pipelines, and tools to support efficient model training and deployment.

Qualifications

• Bachelor’s degree or higher in computer science, electrical engineering, robotics, or a related field.

• 5+ years of experience developing and deploying machine learning models for computer vision or perception applications.

• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.

• Experience with real-time or embedded deployment, including GPU or edge accelerators (e.g., NVIDIA Jetson, Coral, Movidius).

• Familiarity with classical computer vision techniques (OpenCV) and multi-sensor data integration (e.g., cameras, LiDAR, IMU).

• Strong problem-solving skills and ability to work in a collaborative, multidisciplinary environment.

• Experience with robotics, autonomous systems, or real-time perception applications is a plus.

• Knowledge of MLOps practices (e.g., model versioning, CI/CD for ML) is a plus.