Machine Learning Engineer

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 and/or JAX. Familiarity with classical computer vision techniques (e.g., OpenCV). Strong problem-solving skills and ability to work effectively in a collaborative, multidisciplinary environment.

Understanding of software development best practices, including coding standards, code reviews, source control management, and test automation. 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. Experience with camera geometry, 3D reconstruction, or GPU programming (e.g., CUDA, Triton) is a plus. 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 a strong foundation in computer vision and deep learning, experience deploying models into production, and a passion for translating cutting-edge algorithms into real-world robotics applications. The ideal candidate thrives in a multidisciplinary environment and is committed to delivering robust, production-ready solutions.

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