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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.

Job Description

Get to know the Team

The Fulfilment Tech family is one of the pillars that enable Grab to out-serve our consumers and partners in different businesses and marketplaces across Southeast Asia. We are developing high-throughput, real-time distributed systems that use sophisticated 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, thereby increasing the adoption and engagement of our services. 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.

Get to know the Role

As a Lead Machine Learning Engineer, you'll report into the Senior Engineering Manager and work onsite at Grab One North Singapore office. This hands-on role focuses on developing and deploying large-scale user behavioural platforms. The core responsibility involves building advanced behavioural models of our customers, driver, and merchant partners. These models will power personalised recommendation systems, enhancing the experiences of our drivers and merchants.

You'll design and productionise intelligent ML systems to perform large-scale "what if" scenario simulations, predicting aggregate behavioural changes across our users in response to factors like pricing shifts, incentive changes, or fluctuating demand. The resulting insights will be crucial for driving decision-making and shaping policy across the organisation.

The Critical Tasks You Will Perform

• Develop and architect a unified user behavioural platform using the latest Deep Learning (DL) and Large Language Models (LLMs) technologies to model the real-world marketplace behaviour across Grab's customers.
• Design and build state-of-the-art recommendation engines based on user behavioural models to personalise our driver partners' experience on our platform
• Design the User Behavioural Platform to allow comprehensive "What-If" scenario analysis, facilitating data-driven product decisions.
• Define and drive the technical roadmap for integrating the user behavioural platform into more product lifecycles within the Fulfilment Tech Family.
• Set the technical design guidelines for Fulfilment System components to adopt and integrate with the user behavioural platform.
• 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 product managers and engineers to design simulation workflows that support platform policy designs and optimizations.
• Identify and resolve performance bottlenecks and debug model accuracy issues, and improve the model performance.
• 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 at least 2 years of proven experience in DL research, in the domain of LLMs, and at least 2 years of industry experience building complex machine learning services as a core contributor.
• You can develop and integrate sophisticated models, such as those based on Reinforcement Learning (RL), Transformer architectures, or LLMs, to solve real-world business challenges.
• You have experience implementing and improving LLM post-training pipelines: SFT, RL, RLHF.
• You have engineering skills in Python and deep learning frameworks (e.g., PyTorch, Jax, TensorFlow), with experience building high-quality research prototypes and systems.
• You have experience modelling behavioural models of complex recommender systems.
• You have understanding and experience with statistical models like discrete choice modelling (e.g. Mixed Logit for utility maximisation).
• You have an 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.
• You have experience with any big data framework, such as Spark.

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 enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.