About this role
Get to know our Team
The Data Science (GrabMaps) team at Grab focuses on building map-based intelligence such as Place-of-Interest (POI) search and recommendation, data curation, travel time estimation, traffic forecasting, routing, and positioning. Our work powers various Grab services like transport allocation, logistics, and pricing. We extensively use computer vision, natural language processing (NLP), and information retrieval along with conventional machine learning methods on a variety of signals including images, videos, text, sensor readings, and GPS probes to understand places and road networks.
We foster a culture where we enjoy raising the bar constantly for ourselves and others, and strongly support the freedom to explore and innovate.
Duties and Responsibilitie
Collaborate with senior data scientists to adapt and fine-tune LLMs for map-related tasks such as:POI enrichment (e.g., extracting attributes from text/images).Search intent understanding and query rewriting.Knowledge grounding using geo/POI databases.Help design and evaluate agentic AI workflows that integrate LLMs with tools (search APIs, vector databases, map services) to automate tasks like POI validation, deduplication, and categorization.Implement data pipelines for training/evaluating LLM-powered systems, including prompt evaluation, few-shot setups, and fine-tuning.Contribute to prototyping retrieval-augmented generation (RAG) pipelines for map search and recommendation.Perform experiments to measure model accuracy, latency, and robustness, and suggest improvements.Write clean, maintainable code, contribute to shared libraries, and support deployment into production systems.Stay updated with latest literature in LLMs, agent frameworks, and information retrieval, and apply relevant ideas in practical ways. Requirements
Master's or Bachelor's degree in Computer Science, Data Science, AI/ML, or a related fieldHands-on experience with deep learning (PyTorch/TensorFlow) and NLP/LLM frameworks (e.g., HuggingFace, LangChain, LlamaIndex).Strong programming skills in Python; experience with Spark/SQL is a plus.Familiarity with prompt engineering, fine-tuning, or adapting pre-trained LLMs for downstream tasks.Solid understanding of ML fundamentals (classification, ranking, embeddings, evaluation metrics).Ability to work with large-scale datasets, experience with cloud environments (AWS/GCP/Azure) is a plusGood communication and collaboration skills, with an eagerness to learn and experiment.Nice-to-Haves
1–3 years of applied ML/AI experience, ideally with NLP, LLM, or agent systems.Experience with retrieval-augmented generation (RAG), vector databases (Pinecone, Faiss, Milvus), or knowledge graphs.Exposure to multimodal learning (text + images + geo).Familiarity with ML model serving tools (TorchServe, Triton, Ray Serve).Understanding of responsible AI practices such as safety, bias mitigation, and alignment. 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 leaveWe 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 hoursWhat 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.