Senior Data Scientist (LLM Post-Training)

3d3 days ago

Grab

Bengaluru, IN · Full-time · INR 3,000,000 – INR 5,500,000

About this role

Grab is seeking Senior Data Scientists specialising in post-training of large language models to join the team onsite in Bangalore. The role is an individual contributor position reporting to the Head of Data Science, Integrity & COREX.

Work centres on advancing reasoning, multi-modality, multi-lingual systems and AI agents. Solutions target risk and safety measures, customer support, personalised user experiences and internal productivity improvements.

Daily efforts include end-to-end system deployments and close collaboration with business and product teams. The focus remains on creating solutions that deliver measurable commercial impact across Grab's superapp services.

Guided by The Grab Way and its 4Hs principles, the environment blends Heart, Hunger, Honour and Humility. This supports both technical excellence and the broader mission of economic empowerment in Southeast Asia.

Requirements

3 to 8 years of relevant experience with a Master's degree in computer science, artificial intelligence, or a related field.
Solid theoretical foundation in machine learning and deep learning, with in-depth understanding of large models and related technical domains.
Proficient in advanced machine learning and deep learning principles, large-language-model post-training techniques such as SFT (Supervised Fine-Tuning) and DPO (Direct Preference Optimisation).
Experience managing large-scale training data, including processes for cleaning, labelling, augmentation, and generation of domain-specific datasets.
Experience in end-to-end deployment of applications guided by large models, covering development, testing, and launch phases.
Proficiency in Python programming along with competency in widely-used deep learning frameworks (e.g. TensorFlow, PyTorch).
Proficient in spoken and written English to explain technical achievements and collaborate with global teams.

Responsibilities

Implement post-training and optimisation tasks for large language models, including fine-tuning, distillation, performance evaluation, and low-resource training.
Handle domain-specific data processes such as data collection, cleaning, labelling, augmentation, and synthetic data generation for model improvement.
Develop systems that integrate reasoning, multi-modal processing, multi-lingual modelling, and AI agent behaviour for real-world applications.
Deploy LLM-based applications in different scenarios, including risk control, customer support, personalization, and internal productivity use cases.
Prepare technical documentation, patents, prototypes, and demonstrations to showcase project outcomes and advancements.
Engage with business and product teams to ensure agreement on strategic goals and contribute to planning efforts.