Lead Machine Learning Engineer (Fulfilment)

PLT Engineering; Singapore; On-site; SGD 100,000 - 150,000; Full time

Job summary

A leading tech company in Singapore is seeking a Lead Machine Learning Engineer to develop a user behavioural platform aimed at improving marketplace efficiency. You will work with a team to create and refine machine learning models that predict user behavior in response to pricing changes and demand patterns. Ideal candidates have a strong background in machine learning systems, statistical modeling, and experience in software development. The role requires onsite work at Grab One North Singapore office.

Qualifications

• Significant experience developing production-ready Machine Learning systems.
• In-depth knowledge of building behavioural models with multiple agents.
• Experience in software performance analysis and optimization.

Responsibilities

• Develop and architect a user behavioural platform for marketplace behaviour modeling.
• Define the technical roadmap for integrating the user behavioural platform.
• Design the platform for comprehensive 'What-If' scenario analysis.

Skills

Machine learning

Software development life cycle

Statistical modeling

Collaborative teamwork

Education

Degree in Computer Science, Engineering, or related field

Tools

Python

TensorFlow

Docker

Job description

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.