Principal Machine Learning Engineer - AI Platform

About the role

Grab is Southeast Asia's leading superapp, and its AI Platform (AIP) team builds and operates the core ML and AI infrastructure used by hundreds of data scientists and ML engineers, covering model serving, ML pipelines, data serving, AI infrastructure and applied research. As a Principal Machine Learning Engineer on AI Platform, reporting to the AIP Head of Engineering, you will be the senior technical anchor and solution architect for the platform. This is an individual contributor role combining platform engineering, applied ML and user empathy.

Key responsibilities

• Solution architecture: partner with data scientists and ML engineers to design end-to-end solutions on AIP and act as the senior technical escalation point for complex adoption cases.
• Large-scale training: drive throughput, reliability, cost and developer experience, and advise hands-on on foundation model training, RL, simulation and LLM fine-tuning.
• Faster iteration and model quality: shorten the loop from idea to shipped model and improve model quality for key use cases.
• Platform integration: design integrations across model serving, ML pipelines, data serving, AI infrastructure and AI automation tooling.
• User experience translation: turn pain points from user feedback and support into functional requirements and propose cross-platform solutions.
• Enablement at scale: produce reference architectures, patterns and best-practice guidance so new ML use cases land cleanly.
• Roadmap and mentorship: stay current with SOTA in ML infrastructure, LLM serving, training systems and RL, partner with the Head of Engineering on what to adopt, build or skip, and mentor senior engineers.

Required profile

• At least 10 years of experience in Kubernetes, containerization and high-performance computing clusters (GPUs/TPUs), including optimizing large-scale data and model pipelines.
• Ability to work independently in high-ambiguity environments and take full end-to-end accountability for complex integrations.
• Commitment to mentoring senior engineers and raising the engineering bar.

Required skills

• Expert-level MLOps and ML platform engineering, including Kubeflow, MLflow, Triton and TorchServe.
• Distributed training frameworks such as PyTorch, Ray and Horovod.
• Hands-on experience with LLM orchestration, fine-tuning infrastructure or serving optimization (vLLM, TensorRT-LLM).
• Outstanding system design for available, scalable, secure multi-tenant platform services.

What we offer

• Term life insurance and comprehensive medical insurance.
• Parental and birthday leave, plus volunteering leave.
• A confidential assistance programme for employees and their families.
• FlexWork arrangements such as differentiated hours.