Principal Machine Learning Engineer

About Grab

Grab stands as Southeast Asia's premier superapp, supporting users by providing services ranging from meal deliveries to financial management and seamless transportation. Technology and artificial intelligence empower us to fulfill our mission of propelling Southeast Asia’s economy forward, guided by values of heart, hunger, honour, and humility.

Role Overview

Within the AI Platform (AIP) team, we build and maintain the foundational machine learning and AI infrastructure that supports hundreds of data scientists and engineers at Grab. The team is responsible for systems enabling fraud detection, search ranking, foundational model development, adaptive experimentation, LLM fine-tuning, and agent-driven products.

As a Principal Machine Learning Engineer reporting to the Head of Engineering for AIP, you will serve as the chief technical anchor and solution architect, tasked with both elevating the platform’s capabilities and ensuring its usability and adoption across the company.

Key Responsibilities

• Coordinate with data scientists and machine learning engineers to architect comprehensive solutions on the AI Platform, acting as the escalation point for technically complex platform adoption challenges.
• Lead advancements in large-scale model training on the platform, focusing on throughput, reliability, cost efficiency, and developer experience across various workloads such as foundation model training, reinforcement learning, simulation, and LLM fine-tuning.
• Accelerate the workflow from concept to production for models by optimizing data handling, training, evaluation, deployment, and monitoring to reduce iteration cycles and enhance model quality.
• Design and execute integration strategies across different AI Platform components to ensure a unified and seamless user experience.
• Translate user feedback and support data into actionable platform requirements, advocating for improvements that elevate the experiences of data scientists and ML engineers.
• Create scalable enablement material including reference architectures, development patterns, and best practices to support widespread adoption of the AI platform.
• Help define the strategic development roadmap by staying informed about leading-edge ML infrastructure and training methodologies, mentoring senior engineers, and upholding high engineering standards through code and design review.

Required Qualifications and Skills

• Expertise in MLOps and ML platform engineering with comprehensive knowledge of ML lifecycle tools such as Kubeflow, MLflow, Triton, and TorchServe, plus distributed training frameworks including PyTorch, Ray, and Horovod.
• Minimum 10 years experience in distributed systems focusing on Kubernetes, container orchestration, and managing HPC environments with GPUs/TPUs, optimizing large-scale data and model processing pipelines.
• Proven ability in system architecture design emphasizing availability, scalability, security, and multi-tenancy in platform services.
• Hands-on familiarity with AI and LLM systems management, including orchestration, fine-tuning infrastructure, and serving optimization technologies like vLLM and TensorRT-LLM.
• A strong learning orientation to assess and adopt state-of-the-art infrastructure paradigms, providing clear guidance on developments that benefit the platform.
• Adaptability to navigate ambiguous scenarios independently, owning complex integration projects and platform consolidation end-to-end.
• A commitment to enhancing team capabilities by mentoring colleagues and fostering a collaborative culture focused on excellence.

Additional Information: Life at Grab

• Comprehensive Term Life and Medical Insurance.
• Flexible benefits package customization through GrabFlex.
• Parental and birthday leaves, plus community volunteering leave.
• Employee Assistance Program supporting mental health and personal challenges.
• Flexible work arrangements to help balance professional and personal life.

Commitment to Diversity

Grab champions an inclusive and equitable working environment that embraces diversity in all forms and ensures equal opportunity for all candidates regardless of background or personal circumstances.