Principal Machine Learning Engineer, AI Platform – AI Infrastructure

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

Job summary

A leading tech company in Singapore seeks a Principal Machine Learning Engineer to design scalable AI infrastructure. You will lead AI projects, optimize workloads, and mentor teams, shaping the future of AI in the organization. Strong skills in Kubernetes and Ray are needed. Apply now for this pivotal role where you influence AI strategy and execution.

Qualifications

• Proven experience with building scalable systems for machine learning.
• Strong proficiency in Kubernetes and Ray.
• Experience in optimizing AI workloads for performance.

Responsibilities

• Lead and execute AI infrastructure projects.
• Design and scale distributed systems for AI.
• Develop APIs and services for AI experimentation.
• Drive initiatives for cost-efficient AI workloads.
• Integrate research with production systems.
• Influence AI platform strategy.
• Provide mentorship to engineering teams.

Skills

Kubernetes

Ray

Distributed systems

AI Infrastructure

Technical leadership

Job description

Get to Know the Team

The AI Platform team empowers Grab teams to leverage advanced AI seamlessly and effectively. We're building cutting‑edge tools and infrastructure to democratize AI capabilities, accelerate innovation, and enhance Grab's products and services at scale.

Get to Know the Role

As a Principal Machine Learning Engineer focused on AI Infrastructure, you will shape the backbone of Grab's AI ecosystem. You will design and evolve scalable platforms for model training, serving, and evaluation—anchored on technologies like Ray and Kubernetes—that enable thousands of engineers and data scientists to innovate safely and efficiently. Your role is pivotal in ensuring Grab's AI foundation is cost‑efficient, resilient, and future‑ready.

You will report to the Head of Engineering.

This role will be onsite at Grab office.

The Critical Tasks You Will Perform

• Independently Lead and Execute Demonstrate strength as a technical lead by taking full responsibility for projects conception, planning and execution.
• Architect the Future of AI Infrastructure Design and scale the next generation of distributed systems for model training, inference, and experimentation on Kubernetes and Ray.
• Build Platforms for Scale= Develop core abstractions, APIs, and services that make AI experimentation, deployment, and monitoring seamless across Grab.
• Enable Cost‑Efficient AI at Scale Drive initiatives to optimize GPU/CPU utilization, storage, and networking for large‑scale AI workloads, driving significant efficiency gains.
• Integrate Research with Production Systems Translate cutting‑edge distributed training, scheduling, and serving techniques into production‑ready systems that can handle Grab's scale.
• Influence AI Platform Strategy Partner with engineering and product leadership to set direction for Grab's AI infrastructure roadmap, balancing long‑term vision with practical execution.
• Mentor and Inspire Provide deep technical mentorship, foster platform‑thinking, and cultivate a culture of excellence across engineering and research teams.