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.
Requirements
- 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.