About ASUS
AICS is part of ASUS, a multinational company known for the world’s best motherboards, PCs, monitors, graphics cards and routers. Along with an expanding range of superior gaming, content-creation and AIoT solutions, ASUS leads the industry through cutting-edge design and innovations made to create the most ubiquitous, intelligent, heartfelt and joyful smart life for everyone. With a global workforce that includes more than 5,000 R&D professionals, ASUS is driven to become the world’s most admired innovative leading technology enterprise.
About AICS
AICS is a division of ASUS, with the mission to transform healthcare through AI & SaaS. Utilizing deep technologies in Natural Language Processing, Computer Vision, Machine Learning (ML), and Data Analytics, we build and deploy secured solutions that improve the quality of care, increase accessibility, and reduce costs. We have deployed our solutions in over 20 hospitals in Taiwan and plan to expand our services in Singapore and the region.
Responsibilities:
- Hands-on technical leadership and management of day-to-day activities of the ML engineering team within an Agile / Scrum environment.
- Build and grow a best-in-class engineering team.
- Working closely with the engineers to architect and develop the best ML solutions on the public cloud, including models, pipelines, performance optimization, testing and deployment.
- Collaborate across engineering and product teams to translate business needs into ML specifications and deliverables.
- Work closely with an entrepreneurial team of experienced Researchers and Software Engineers to successfully ship software products and continue to grow our business.
- Management of departmental resources, staffing and mentoring.
- Any other tasks as and when assigned by the management.
Requirements:
- Bachelor’s and/or Master’s degree in Computer Science, Computer Engineering, Electric Engineering.
- 8+ years of building successful machine learning software solutions.
- 3+ years of experience managing ML and software engineers.
- Strong machine learning and deep learning fundamentals.
- Proficiency in developing and deploying ML solutions on Azure or other public cloud providers.
- Understanding of best practices in MLOps and software development processes including coding standards, code reviews, data and model pipelines, source control (Github), and test automation/CICD.
