Grab Hiring! Full Time Lead Machine Learning Engineer (Foundation Models) in - Ricebowl

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

1. 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.
2. 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.
3. 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.
4. Optimize Model Architectures: Design, customize, and implement highly efficient transformer architectures tailored for Grab's business use cases, including Mixture-of-Experts (MoE), long-context attention, and tokenization strategies.
5. Enforce Engineering Excellence: Lead code and design reviews, establish high-standard engineering patterns, and mentor junior engineers to sustain, testable, and production-grade codebases.
6. 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.

What Essential Skills You Will Need

• Industry Experience: At least 8 years of professional experience in machine learning, with a deep focus on deep learning, transformer-based architectures, and the software development lifecycle.
• Pre-training Expertise: Proven hands-on track record of pre-training or continually pre-training open foundation models (e.g., Llama, Qwen, DeepSeek, Mistral) from scratch, rather than solely utilizing third-party APIs.
• Distributed Systems Mastery: Deep technical proficiency with multi-node, multi-GPU scaling frameworks such as PyTorch FSDP, DeepSpeed, Megatron-LM, and Ray, along with an understanding of modern hardware accelerators.
• Post-Training & Alignment: Practical experience in LLM post-training methodologies, including Supervised Fine-Tuning (SFT), Parameter-Efficient Fine-Tuning (LoRA/QLoRA), and preference alignment methods (RLHF, DPO, RLAIF).
• Modern Software Engineering: High proficiency in writing clean, maintainable, and testable production code in Python/C++, with solid experience in MLOps/LLMOps deployment pipelines.
• AI & Productivity Adoption: You have experience engaging with AI tools and emerging technologies (such as LLM assistants, code generators, and specialized developer agents) to enhance personal productivity, optimize engineering workflows, and contribute innovative platform ideas.
• Generative Recommendation: Exposure to generative retrieval, semantic tokenization, sequence modelling of user behaviour, or unifying retrieval and ranking with preference-aligned models is a plus.