Senior Principal Machine Learning Engineer

GrabTaxi Holdings Pte. Ltd.; Singapore; On-site; SGD 120,000 - 160,000; Full time

Job summary

A leading technology company in Singapore is seeking a Senior Principal Machine Learning Engineer to lead fulfilment strategy optimization and driver behaviour modelling. You will develop advanced machine learning models to enhance decision-making across various platforms. The role requires a Master's degree in computer science and at least 10 years of experience, focusing on reinforcement learning and multi-agent systems. This position offers comprehensive benefits and a collaborative environment.

Benefits

Term Life Insurance

Comprehensive Medical Insurance

GrabFlex benefits package

Parental and Birthday leave

Grabber Assistance Programme

FlexWork arrangements

Qualifications

• At least 10 years of relevant experience.
• Experience developing and deploying ML models.
• Familiarity with distributed computing systems.

Responsibilities

• Apply advanced ML models to enhance performance.
• Develop RL architectures for optimizing decisions.
• Collaborate with teams to validate models.

Skills

Reinforcement Learning

Machine Learning

Python

Behavioural Modelling

Education

Master's degree in Computer Science or related field

Tools

PyTorch

TensorFlow

Spark

Job description

About Grab and Our Workplace

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. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility.

Get to Know the Team

The Fulfilment Tech Family is a foundational part of Grab, enabling seamless coordination between our diverse marketplaces across Southeast Asia. We design real-time, distributed systems and machine learning solutions to process hundreds of millions of requests per day, driving efficient supply allocation, pricing, and order matching. Our mission: Deliver best-in-class products for our driver-partners. Maximise efficiency in fulfilling consumer demand – rain or shine. Create sustainable, efficient marketplaces that balance experience and cost for all stakeholders. We're seeking a Senior Principal Machine Learning Engineer to join our Fulfilment team and take the lead in Fulfilment strategy optimization and driver behaviour modelling – a strategic area for understanding our partners and optimising our pricing, dispatch and supply management policies.

Get to Know the Role

You'll report to the Head of Engineering and work onsite at Grab's One North Singapore office. You'll focus on optimising cross-system fulfilment strategies and modelling driver responses to different platform interventions. This includes leading development in reinforcement learning (RL), behavioural prediction, and simulation-based optimization techniques, aimed at improving operations across our marketplace.

Your work will involve building interpretable, adaptable multi-agent RL systems or decision agents that consider multiple objectives while handling disruptions. You'll also develop high-fidelity models that simulate marketplace operations, aiding teams in designing smarter algorithms and driving impactful product decisions.

The Critical Tasks You Will Perform

You will:

• Apply advanced ML/ DL models to enhance performance and improve generalisation and efficiency
• Develop unified RL architectures that coordinate multiple levers (pricing, dispatching, and supply planning) with differing objectives and time scales.
• Build multi-agent or hierarchical RL frameworks to jointly optimize pricing, dispatching, and repositioning decisions, pushing the marketplace Pareto frontier.
• Research scalable representations of marketplace state that incorporate supply-demand signals, elasticity, traffic, weather, and driver intent
• Develop robust, interpretable models to capture driver decision-making under varying operational conditions.
• Design feedback loops that adapt to driver behaviour over time and across geographies.
• Collaborate with platform and experimentation teams to run real-world validations and iterate on model design.
• Build tooling and simulations to support counterfactual analysis and platform design decisions.
• Operate as a technical lead, guiding data scientists in these efforts while fostering a collaborative and high-performance environment
• Work with data engineers and backend engineers to integrate optimization models into real-time production systems.
• Support the broader roadmap of supply planning, pricing, dispatch, and marketplace experimentation across Fulfilment.

What Essential Skills You Will Need

• You have a Master's degree in Computer Science, Operations Research, Applied Mathematics, or related field with at least 10 years of relevant experience.
• You have experience with Reinforcement Learning, MDPs, stochastic control, or behavioural modelling under uncertainty.
• You have experience developing and deploying ML models that incorporate online learning, temporal decision processes, or simulation-based optimization.
• You are fluent in Python and ML frameworks (e.g., PyTorch, TensorFlow).
• You are familiar with distributed computing systems or scalable training platforms (e.g., Spark, Ray).
• You can translate high-level business problems into tractable modelling tasks.

Life at Grab

We care about your well-being at Grab, here are some of the global benefits we offer:

• We have your back with Term Life Insurance and comprehensive Medical Insurance.
• With GrabFlex, create a benefits package that suits your needs and aspirations.
• Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
• We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges.
• Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours

What We Stand For At Grab

We are committed to building an inclusive and equitable workplace that provides equal opportunity for Grabbers to grow and perform at their best. We consider all candidates fairly and equally regardless of nationality, ethnicity, race, religion, age, gender, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.