Engineering Manager

1 week ago

Singapore ASUS GLOBAL PTE. LTD. Full time

Are you ready to ignite your career and be part of something truly extraordinary? At ASUS, we’re In Search of Incredible—and we want you on our team We’re looking for dynamic, passionate individuals who are eager to innovate, create, and make waves in the tech world. If you’re fueled by ambition and thrive in a fast-paced environment where your ideas can shape the future, this is your opportunity to shine.

Join us, and let’s chase the incredible together

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.

**The 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.
- Work 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 with an entrepreneurial team of experienced AI researchers and software engineers to successfully ship software products and continue to grow our business.
- Report on the status of development, quality, operations, and system performance to management.
- Management of departmental resources, staffing, and mentoring.

**The 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

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