Lead Machine Learning Engineer, Foundation Models at Grab

Get to know our Team The AI Automation Team is dedicated to applied research in cutting-edge technologies and the development of highly scalable Foundation World Models that allow our AI systems to understand, simulate, and predict the complex temporal and spatial dynamics of Grab's marketplace across a wide variety of domains. Our core research areas encompass foundation models, embeddings, world models, and reinforcement learning. We are looking for experienced machine learning engineers to join our team and contribute to realizing this vision. Get to know the Role This is an applied research role aimed at developing foundation world model solutions for Grab. We are seeking a visionary Lead Machine Learning Engineer to spearhead our Foundation World Models team. In this role, you will lead the design, pre-training, and adaptation of massive, multi-modal foundation models that simulate physical environments. You will guide a team of ML engineers and researchers to build generalized, foundational architectures capable of zero-shot and few-shot spatio-temporal reasoning and action-conditioned environment simulation. You'll report into the Senior Engineering Manager and be based onsite at Grab One North Singapore office. The Critical Tasks You will Perform • Lead the end-to-end development of foundational world models, making critical architectural decisions regarding transformers, diffusion models, state-space models (e.g., Mamba), or latent variable models. • Design and optimize distributed training pipelines for massive multimodal data sets using multi-node GPU clusters. • Stay at the forefront of AI research. Translate state-of-the-art academic papers in foundation model, world modelling (e.g., Sora, JEPA, Dreamer), and reinforcement learning into scalable, production-ready code. • Work with MLOps and systems engineers to optimize model inference, ensuring low-latency and high-throughput for downstream tasks. • Collaborate with cross-functional teams to understand their requirements and integrate foundation world models into applications. • Mentor team members on foundation world model development and best practices.