Description
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.
The AI Platform (AIP) team builds and operates the core ML and AI infrastructure that powers Grab. As a Principal Machine Learning Engineer on AI Platform, you'll be the senior technical anchor and solution architect for the platform. Your mandate is twofold: (1) Raise the ceiling and raise the floor. You'll partner with the Head of Engineering, AI Platform and Applied Research team to shape the forward roadmap, evaluate SOTA techniques worth productionizing, and drive integrations that connect AIP's capabilities into a coherent end-to-end experience. (2) You'll ensure the teams who depend on AIP can actually succeed on it , translating user pain into platform requirements, unblocking complex adoption cases, and getting hands-on where it matters.
The Critical Tasks You Will Perform:
- Solution Architecture for AIP Users: Partner directly with Data Scientists and ML engineers across the company to design end-to-end solutions on AIP.
- Large-Scale Training: Drive the state of large-scale training on AIP , throughput, reliability, cost, and developer experience.
- Faster Iteration and Model Quality: Attack the end-to-end loop from idea to shipped model , data, training, evaluation, deployment, monitoring.
- Platform Integration: Design and drive integrations across AIP surfaces (model serving, ML pipelines, data serving, AI infra, AI Automation tooling).
- User Experience Translation: Convert pain points surfaced through embeddings, support channels, and direct user work into functional requirements for AIP teams.
- Enablement at Scale: Produce reference architectures, patterns, and opinionated best-practice guidance.
- Strategic Roadmap Definition & Mentorship: Stay current with SOTA across ML infrastructure, LLM serving, training systems, and RL.
QUALIFICATIONS:
- Advanced MLOps & ML Platform Engineering: Expert-level mastery of ML lifecycle platforms (e.g., Kubeflow, MLflow, Triton, TorchServe) and distributed training frameworks (PyTorch, Ray, Horovod).
- Distributed Systems & Infrastructure: At least 10 years of experience in Kubernetes, containerization, and high-performance computing clusters (GPUs/TPUs).
- Architecture Design: Outstanding system design capability for available, scalable, and secure multi-tenant platform services.
- AI/LLM System Experience: Hands-on experience with LLM orchestration, fine-tuning infrastructure, or serving optimization.
- Innovation & AI Fluency: A learning mindset to evaluate and implement state-of-the-art (SOTA) infrastructure paradigms.
- Adaptive Execution & Ownership: You can operate independently in high-ambiguity environments.
- Coaching with Care: Commitment to raising the engineering bar.
Life at Grab:
- Term Life Insurance and comprehensive Medical Insurance.
- GrabFlex benefits package.
- Parental and Birthday leave.
- Love-all-Serve-all (LASA) volunteering leave.
- Confidential Grabber Assistance Programme.
- FlexWork arrangements.