Senior Data Scientist

Get to Know the Team

The Data Science (GrabMaps) team at Grab focuses on building map-based intelligence such as POI search and recommendation, data curation, ETA, traffic forecasting, routing, and positioning. Our work powers multiple Grab services like transport allocation, logistics, and pricing. We use computer vision, 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 also help develop scalable models through deep research to delight our customers with intelligent products. We foster a culture that supports the freedom to explore and innovate.

Get to Know the Role

You will report to Lead Data Scientist and be based at Chengao Plaza, Chaoyang District, Beijing.

The Critical Tasks You Will Perform

• Identify areas for investigation, translate them to technical problems to be solved, explain solutions to tech and non-tech team members
• Oversee end-to-end small/moderate products/services from design to production rollout
• Define hypotheses, develop necessary tests, experiments, and data analyses to prove or disprove them independently
• Develop, and increase deep learning and machine learning algorithms—including generative AI, Large Language Models (LLMs), and multi-modal models—for real-world impact
• Fine-tune, evaluate, and adapt LLMs (e.g., GPT, Llama, Qwen) and other foundation models using both supervised and reinforcement learning approaches
• Architect agentic AI workflows using modern orchestration frameworks (e.g., LangChain, LlamaIndex, OpenAI Function Calling), including tool integration, chaining, and multi-agent coordination
• Contribute to team's innovation and IP creation
• Keep up with the latest literature in Search / Recommendation, Natural Language Processing/LLMs or Computer Vision

Qualifications

What Essential Skills You Will Need

• Master in Computer Science, Electrical/Computer Engineering, Operations Research.
• Hands-on experience in deep learning and AI, with expertise in LLMs including fine-tuning, prompt engineering, and adapting foundation models for downstream tasks
• Demonstrated experience deploying LLMs and other large-scale AI models to production:
• 1+ years of experience serving LLMs and agentic systems in production environments (e.g., TorchServe, Triton, or Ray Serve)
• Knowledge of model compression, quantization, and techniques for optimizing inference latency and cost
• Familiarity with GPU/TPU acceleration and distributed inference architectures
• Experience implementing and maintaining scalable pipelines for data preprocessing, model training, fine-tuning, and automated evaluation
• Proficiency in deep learning frameworks (TensorFlow, PyTorch) and deployment tools (ONNX, tf-serving, TorchServe, Triton Inference Server)
• Solid software engineering skills in Python/Spark; knowledge of GoLang or Rust
• Experience with model versioning, CI/CD for ML, containerization (e.g., Docker), and cloud-based deployment (AWS, GCP, Azure)

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.