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 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.

• →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.

• →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.