Senior Principal Machine Learning Engineer (Fulfilment)

Grab is Southeast Asia's leading superapp, providing services such as food delivery, financial management, and ride-hailing.

The Fulfilment Tech Family builds the systems that power Grab's marketplaces across Southeast Asia. We design real-time, distributed systems and Machine Learning (ML) solutions that process hundreds of millions of requests each day. Our work drives supply allocation, pricing, and order matching for millions of users and driver-partners.

We are looking for a Senior Principal Machine Learning Engineer to lead our shift toward automated marketplace optimization. You'll advance how we use data and ML to automate pricing, dispatch, and supply management decisions.

Responsibilities

• Design and implement end-to-end RL systems that combine model-based RL, offline RL, simulation, and online learning into a unified training pipeline.
• Build latent world models and marketplace state representations that capture supply-demand interactions, location-based patterns, and behavioural signals from users and drivers.
• Develop systems that optimize across multiple marketplace levers simultaneously,pricing, dispatching, and supply repositioning,to expand the set of achievable outcomes for the business.
• Create policy evaluation frameworks and establish monitoring systems that allow safe deployment of new decision-making policies in production.
• Fine-tune open-source large language models on domain-specific data to build capabilities for prediction, reasoning, and simulation within marketplace applications.
• Design and implement training strategies for language models, including supervised fine-tuning, preference-based alignment, and iterative improvement methods.
• Work with data engineers and backend engineers to integrate RL and LLM systems into real-time production environments serving millions of users.

Qualifications

• PhD in Computer Science, Operations Research, Applied Mathematics, or related field with at least 10 years of experience.
• Proficiency in RL fundamentals, including Markov Decision Processes, stochastic control, and reward design trade-offs.
• Experience building production ML/RL systems with online learning or simulation-based optimization.
• Knowledge in world models and sequential modelling, including latent dynamic models and representation learning for complex systems.
• Hands-on experience fine-tuning large language models in production.
• Experience with distributed training frameworks, such as DeepSpeed, FSDP, or Megatron-LM.
• Proficient in Python and ML frameworks, such as PyTorch or TensorFlow.
• Experience with scalable computing platforms, such as Spark or Ray.

Benefits

• Term Life Insurance and comprehensive Medical Insurance.
• GrabFlex, a benefits package that suits your needs and aspirations.
• Parental and Birthday leave.
• Love-all-Serve-all (LASA) volunteering leave.
• Confidential Grabber Assistance Programme.
• FlexWork arrangements, such as differentiated hours.