Machine Learning Engineering Manager

ASUS; Singapore; On-site; SGD 120,000 - 150,000; Full time

Job summary

A leading tech firm is looking for a dynamic ML Engineering Manager in Singapore. You will lead a team to build innovative machine learning solutions using cloud technologies. Ideal candidates have extensive experience in ML and team management, and a strong educational background in Computer Science or related fields. This role offers the opportunity to make an impact in healthcare through advanced AI technologies.

Qualifications

• 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.

Responsibilities

• Technical leadership and management of the ML engineering team.
• Build and grow a best-in-class engineering team.
• Collaborate with engineering and product teams to deliver ML solutions.

Skills

Machine learning fundamentals

Leadership

Data processing

Cloud solutions

Education

Bachelor’s and/or Master’s degree in Computer Science, Computer Engineering, or Electrical Engineering

Tools

Azure

GitHub

TensorFlow

PyTorch

Job description

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, or Electrical 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.

• At least 7 years of experience in Machine Learning, Data Science, or AI.
• At least 3 years of experience in managing and leading teams in a technical environment.
• Proven track record of building and deploying machine learning models in production.
• Proficiency in software development best practices, including adherence to coding standards, conducting code reviews, applying design patterns, managing source control (e.g., GitHub), and implementing test automation and CI/CD pipelines.
• Experience with large-scale data processing, cloud platforms (AWS, Google Cloud, Azure), and Machine Learning frameworks (TensorFlow, PyTorch).