Senior Data Scientist (GrabMaps)

Get to Know the Team

The Data Science (GrabMaps) team builds map intelligence that powers core Grab services like transport allocation logistics and pricing. Youll work on problems such as place search and recommendation data curation travel time estimation traffic forecasting routing and positioning.

The team invests in deep research and scalable models and youll have room to explore new ideas that can shape how millions of users experience Grabs products.

Get to Know the Role

As an Applied Scientist for GrabMaps youll design build and ship machine learning and generative AI solutions that directly impact how Grab understands and uses map data. Youll work across the full lifecycle: framing problems with stakeholders developing models (including LLMs and multimodal models) and deploying them into production.

Youll focus on using large modelsLLMs vision and multimodal modelsto improve search recommendation and content understanding around places and road networks.

The Critical Tasks You Will Perform

You will:

• Translate business problems in mapping search and recommendation into clear machine learning problems define success metrics and explain your approach and results to both technical and nontechnical stakeholders.
• Own endtoend delivery of small to mediumscope ML/LLM features or services from data exploration and model design through training evaluation deployment and postlaunch monitoring.
• Develop and optimize deep learning modelsincluding LLMs generative and multimodal modelsto solve use cases such as POI understanding relevance ranking content generation and map data quality.
• Finetune evaluate and adapt stateoftheart LLMs (e.g. GPT Llama Qwen) and other foundation models using supervised finetuning and RLbased methods including prompt and instruction design for downstream tasks.
• Architect and implement agentic AI workflows (for example with LangChain LlamaIndex or functioncalling APIs) including tool integration workflow chaining and multiagent coordination for realtime or near realtime applications.
• Build and maintain scalable pipelines for data preprocessing feature extraction model training finetuning automated evaluation and model versioning working with ML engineers and software engineers to run them in production.
• Optimize model serving for latency throughput and cost using techniques such as model compression quantization GPU/TPU acceleration and distributed inference and integrate with serving frameworks like TorchServe Triton or Ray Serve.
• Review relevant research in search/recommendation NLP/LLMs and computer vision run targeted experiments and bring promising ideas into production prototypes or features.

Qualifications :

What Essential Skills You Will Need

• Ph.D. in Computer Science Electrical/Computer Engineering Operations Research or a related field or Masters with at least 2 year of equivalent practical experience applying advanced machine learning methods to realworld problems.
• 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:
  • Experience serving LLMs and agentic systems in production environments (e.g. TorchServe Triton Ray Serve)
    • Knowledge of model compression quantization and techniques for optimising 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 etc.)
• Solid software engineering skills in Python/Spark
• 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 lifes challenges.
• Balancing personal commitments and lifes 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.

Remote Work :

No

Employment Type :

Full-time