Senior Machine Learning Engineer

Bachelor's degree or higher in computer science, electrical engineering, robotics, or a related field. 8+ 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. Hands-on experience training models such as VLMs, ViTs, and/or CNNs. Familiarity with classical computer vision techniques (e.g., OpenCV).

Exceptionally strong problem-solving skills and the ability to lead technical work in a fast-paced, 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 strongly preferred. 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 Senior Machine Learning Engineer to join our global research and development team. This role centers on leading the design and delivery of advanced computer vision and perception systems for our next-generation autonomous platforms. We are looking for a seasoned engineer who brings deep technical expertise, sound engineering judgment, and a proven track record of delivering production ML systems in complex, real-world environments.

The ideal candidate is a mature, self-directed contributor who can drive technical decisions, mentor others, and operate effectively across a collaborative, multidisciplinary team. Lead the design and development of machine learning systems for computer vision and perception tasks. Optimize models for real-time performance on embedded and edge computing platforms. Build and maintain robust 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. Drive technical decisions and provide guidance on architecture, tooling, and best practices across the team. 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.