Lead Machine Learning Engineer (Foundation Models)
Summary
Lead the development of Grab’s proprietary foundation models and generative recommendation systems, scaling distributed training and deploying AI solutions for millions of users.
Get to Know the Team
Join the AI Automation Team at Grab—a high-impact, pioneering applied research group shaping the future of Grab's superapp marketplace across Southeast Asia. Our mission is to develop and scale next‑generation ML/AI solutions that solve complex structural challenges. We focus on building proprietary foundation models, pioneering generative recommendation systems, large‑scale reinforcement learning, and LLM post‑training. By joining us, you will work with a world‑class team of researchers and engineers to turn bleeding‑edge AI research into physical‑world superapp impact. Get to Know the Role
This is an applied research and machine learning engineering role aimed at developing foundation model solutions for Grab's massive marketplace. As a Lead Machine Learning Engineer (based onsite in our Singapore One‑North headquarters, reporting to the Senior Machine Learning Engineering Manager), you will own the end‑to‑end lifecycle of our proprietary foundation models and generative recommenders. You will bridge the gap between modern ML research and optimised, large‑scale distributed training infrastructure. You will build architectures that unify search, retrieval, and ranking to power decisions for millions of users daily. The Critical Tasks You Will Perform
Architect Pre‑training Pipelines:
Design, implement, and orchestrate efficient, scalable pre‑training pipelines for large language and multimodal foundation models, from raw data curation and tokenization through to converged checkpoints. Build Distributed Training Systems:
Develop and operate large‑scale distributed training frameworks across multi‑node GPU clusters, applying advanced parallelism strategies (FSDP, DeepSpeed, Megatron‑LM, Tensor/Pipeline/Expert parallelism) and memory‑efficient training techniques. Pioneer Generative Recommendation:
Design and deploy generative recommendation systems that combine foundation model pre‑training with a full post‑training loop (SFT, distillation, preference alignment and RL) to unify retrieval and ranking against marketplace objectives. Optimize Model Architectures:
Design, customise, and implement highly efficient transformer architectures tailored for Grab's business use cases, including Mixture‑of‑Experts (MoE), long‑context attention, and tokenisation strategies. Enforce Engineering Excellence:
Lead code and design reviews, establish high‑standard engineering patterns, and mentor junior engineers to sustain, testable, and production‑grade codebases. Bridge Research and Production:
Partner with cross‑functional product, platform, and infrastructure teams to integrate custom foundation models and generative recommenders into live, highly‑available production environments.
Join the AI Automation Team at Grab—a high-impact, pioneering applied research group shaping the future of Grab's superapp marketplace across Southeast Asia. Our mission is to develop and scale next‑generation ML/AI solutions that solve complex structural challenges. We focus on building proprietary foundation models, pioneering generative recommendation systems, large‑scale reinforcement learning, and LLM post‑training. By joining us, you will work with a world‑class team of researchers and engineers to turn bleeding‑edge AI research into physical‑world superapp impact. Get to Know the Role
This is an applied research and machine learning engineering role aimed at developing foundation model solutions for Grab's massive marketplace. As a Lead Machine Learning Engineer (based onsite in our Singapore One‑North headquarters, reporting to the Senior Machine Learning Engineering Manager), you will own the end‑to‑end lifecycle of our proprietary foundation models and generative recommenders. You will bridge the gap between modern ML research and optimised, large‑scale distributed training infrastructure. You will build architectures that unify search, retrieval, and ranking to power decisions for millions of users daily. The Critical Tasks You Will Perform
Architect Pre‑training Pipelines:
Design, implement, and orchestrate efficient, scalable pre‑training pipelines for large language and multimodal foundation models, from raw data curation and tokenization through to converged checkpoints. Build Distributed Training Systems:
Develop and operate large‑scale distributed training frameworks across multi‑node GPU clusters, applying advanced parallelism strategies (FSDP, DeepSpeed, Megatron‑LM, Tensor/Pipeline/Expert parallelism) and memory‑efficient training techniques. Pioneer Generative Recommendation:
Design and deploy generative recommendation systems that combine foundation model pre‑training with a full post‑training loop (SFT, distillation, preference alignment and RL) to unify retrieval and ranking against marketplace objectives. Optimize Model Architectures:
Design, customise, and implement highly efficient transformer architectures tailored for Grab's business use cases, including Mixture‑of‑Experts (MoE), long‑context attention, and tokenisation strategies. Enforce Engineering Excellence:
Lead code and design reviews, establish high‑standard engineering patterns, and mentor junior engineers to sustain, testable, and production‑grade codebases. Bridge Research and Production:
Partner with cross‑functional product, platform, and infrastructure teams to integrate custom foundation models and generative recommenders into live, highly‑available production environments.