Job Description

Company 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 one of the pillars that allow Grab to out-serve our consumers and partners in various businesses and marketplaces across Southeast Asia. We are developing high-throughput, real-time distributed systems that use machine learning techniques to handle hundreds of millions of requests per day. Our mission is to provide the best-in-class products and experiences to our driver partners, improve driver partner opportunities and efficiency to fulfil consumer orders without fail, rain or shine, and to create efficient marketplaces by determining an optimal price that is both sustainable and loved by our partners and consumers.
At the Fulfilment machine engineering team, we are working to solve challenging problems in the marketplace that involve dynamic pricing and supply and demand management. We're looking for a Lead Machine Learning Engineer to join our team and help bring that vision to life by developing and refining cutting-edge reinforcement learning models and simulation platforms.
Get to know the Role
This is a hands-on role focused on building large-scale user behavioural platforms. You'll be reporting to the Senior Engineering Manager and work onsite at Grab One North Singapore office. You'll focus on large-scale behavioural modeling of our customers, drivers and merchant partners. You'll design and productionise intelligent ML systems that will provide us answer to questions such as "how drivers will respond to changes in pricing, incentives, wait times, or demand patterns in different contexts".
You understand the software development lifecycle and engineering best practices, along with significant experience developing production-ready Machine Learning systems. You have in-depth knowledge of building behavioural models of complex systems consisting of multiple agents.
The Critical Tasks You Will Perform

• Develop and architect a user behavioural platform to model the real-world marketplace behaviour across Grab's customers, driver and merchant partners.
• Define and drive the technical roadmap for integrating the user behavioural platform into the product development lifecycle within the Fulfilment Tech Family.
• Set the technical design guidelines for Fulfilment System components to adopt and integrate with the user behavioural platform.
• Design the User Behavioural Platform to allow comprehensive "What-If" scenario analysis, facilitating data-driven product decisions.
• Develop and integrate both statistical models (e.g., Mixed Logit for utility maximization and discrete choice) and advanced generative models (e.g., RL, Transformer-based, or LLM-driven agents) for modeling user/driver action sequences and responses to platform changes.
• Collaborate with data scientists and engineers to design simulation workflows that support platform policy designs and optimizations.
• Design and scale the user behavioural platform to execute hundreds to thousands of behavioural predictions daily.
• Identify and resolve performance bottlenecks and debug model accuracy issues.
• Conduct service capacity and demand planning, software performance analysis, costing, tuning, and optimization.
• Participate in code and design reviews to uphold high development standards.

Qualifications
What Essential Skills You Will Need

• You have a Degree in Computer Science or related fields and at least 6 years of experience in Software Engineering for complex Machine Learning services
• You are proficient in Python and experience with ML framework, such as TensorFlow or PyTorch and big data framework, such as Spark.
• You have in-depth knowledge of building behavioral models of complex systems
• You have experience with statistical models like discrete choice modelling (e.g. Mixed Logit for utility maximisation)
• You have experience developing and integrating advanced models, including Reinforcement Learning (RL), Transformer-based agents, or LLM-driven agents for modelling user/ driver action sequences
• You have experience implementing and improving LLM post-training pipelines: SFT, RL, RLHF
• You have understanding of software engineering practices and design patterns, experience writing readable, maintainable and testable code
• You have experience turning business problems into ML/ AI-projects
• You have experience developing and productionising ML Pipelines using modern technologies such as Airflow, MLFlow

Additional Information
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.

Skills Required