Get to Know the Team
You will join our Search and Personalization team — a group of engineers and data scientists building the intelligence that helps millions of users discover and engage with food, groceries, mobility, and services across Southeast Asia. We work with engineering, product, and business teams to develop foundational machine learning capabilities that allow Grab to better understand users and deliver relevant experiences across the platform.
Get to Know the Role
Reporting to the Head of Data Science, Business Ecosystem, you'll be based onsite in the Grab One North Singapore office. This is a Principal Data Scientist and senior individual contributor role in the Search and Personalization team, focusing on user understanding platformization and generative recommendation.
You will provide technical leadership in building shared user understanding capabilities that power personalized experiences across Grab, including search, recommendation, chatbot, notification, and other user-facing applications. You will work across Data Science, Engineering, and Product to shape the long-term technical direction, turn latest artificial intelligence research into practical solutions, and drive adoption of these capabilities across teams.
The Critical Tasks You Will Perform
- You will define the technical strategy and roadmap for user understanding and generative personalization, identifying foundational ML capabilities that can be shared across search, recommendation, chatbot, and other personalized experiences.
- You will architect and develop large-scale user understanding systems that capture long-term preferences, real time intent, behavioral patterns, interests, and contextual signals from heterogeneous user interactions across Grab.
- You will develop reusable user representations and foundation models that support diverse downstream tasks, including retrieval, ranking, recommendation, conversational personalization, targeting, and engagement.
- You will advance generative recommendation, exploring foundation models, generative retrieval, sequence modeling, and unified user-item representations to complement and evolve conventional retrieval and ranking architectures.
- You will drive the platformization of user understanding, turning successful modeling approaches into reusable representations, models, features, APIs, and serving systems adopted by multiple teams.
- You will establish rigorous offline and online evaluation methodologies to measure the quality, generalizability, and incremental impact of user understanding and generative models across downstream applications.
- You will partner with senior machine learning and platform engineers to design production architectures for large-scale model training, representation generation, real-time user understanding, and low-latency serving.
- You will evaluate emerging research in generative recommendation, foundation models, user modeling, generative retrieval, and representation learning, and lead promising approaches from research and prototypes into production.
What Essential Skills You Will Need
- Master's Degree in Computer Science, Machine Learning, Artificial Intelligence, Operations Research, or another quantitative field, or equivalent practical experience developing large-scale machine learning systems.
- At least 7 years of relevant industry experience, including substantial experience designing and deploying production deep learning systems for recommendation, search, user modelling, NLP, or related applications at scale.
- Expertise and technical leadership in user modelling, recommendation systems, information retrieval, representation learning, or sequence modelling, with experience transforming large-scale behavioural data into representations that generalise across multiple downstream tasks (Theory and domain)
- Expertise with modern deep learning architectures, including Transformers and sequence-based models, with hands-on proficiency in frameworks such as PyTorch, TensorFlow.
- Experience designing large-scale ML systems, including distributed training, feature and embedding computation, real-time user representations, online inference, latency optimization, model monitoring, and retraining.
- Proficiency in Python and experience with large-scale data processing technologies such as Spark, Scala, or equivalent distributed data systems
- Experience with experimentation and evaluation, including offline evaluation, A/B testing, metric design, and measuring the incremental impact of shared models and representations across heterogeneous downstream applications
- You can bridge latest research and production systems — particularly in generative recommendation and foundation models — and identify where new modelling approaches provide meaningful advantages over established architectures
- You can define technical direction in ambiguous problem spaces and influence senior engineers, data scientists, product managers, and leaders across organisational boundaries.
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.