Principal Machine Learning Engineer, AI Platform – AI Infrastructure at Kudo

Singapore, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

28 Dec, 25

Salary

0.0

Posted On

29 Sep, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, AI Infrastructure, Distributed Systems, Kubernetes, Ray, Cloud Infrastructure, Python, APIs, Microservices, Observability, CI/CD, GPU Scheduling, Cost Optimization, Technical Leadership, Mentorship, Platform Engineering

Industry

Software Development

Description

Company Description About Grab and Our Workplace Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility. 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. Qualifications What Essential Skills You Will Need Experience 6+ years of experience building large-scale AI/ML or distributed systems infrastructure. At least 2 years in a technical leadership capacity, driving architectural decisions and mentoring teams. Deep Infrastructure & Distributed Systems Expertise Hands-on experience with Ray (Ray Train, Ray Serve, Ray Tune) and distributed data processing frameworks (e.g., Dask, Spark). Expertise in Kubernetes, container orchestration, autoscaling, and cloud-native architectures. Systems & Platform Engineering Experience designing and delivering developer platforms that abstract away complexity while ensuring scale. Background in APIs, microservices, observability, and CI/CD best practices. Cloud & Compute Optimization Experience running large-scale AI/ML workloads on cloud infrastructure (AWS/GCP/Azure). Expertise in GPU scheduling, heterogeneous clusters, and cost-optimization strategies. Programming & Engineering Excellence Proficiency in Python and one or more system-level languages (e.g., Go, Rust, C++). Strong engineering fundamentals in concurrency, networking, storage, and system performance. Strategic Visionary & Leadership Strategic AI Infrastructure Leadership: Develops roadmaps that align AI infrastructure with core business priorities. Platform Empowerment: Passionate about building platforms that accelerate impact for engineers, researchers, and product teams. Influence & Mentorship: Influence technical direction across diverse teams and a strong track record of mentoring engineers. Additional Information Life at Grab We care about your well-being at Grab, here are some of the global benefits we offer: We have your back with Term Life Insurance and comprehensive Medical Insurance. With GrabFlex, create a benefits package that suits your needs and aspirations. Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges. Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours What We Stand For at Grab We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.

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Responsibilities

As a Principal Machine Learning Engineer, you will design and evolve scalable platforms for model training, serving, and evaluation. You will lead initiatives to optimize AI workloads and ensure the AI foundation is cost-efficient and resilient.