Job Description

Job Title: Senior Machine Learning Engineer

Company: Grab

Years of Experience Required: 2–5+ Years (including hands-on LLM deployment experience)

Location: Beijing, China (Chengao Plaza, Chaoyang District)

Role Type: Full-Time, Onsite

Salary: Competitive and commensurate with experience

Eligibility:

Candidates must have hands-on experience with deep learning, multi-modal AI systems, and large language models, with proven deployment experience in production environments.

Role Overview

This role sits within Grab’s Data Science (GrabMaps) organisation and focuses on building, scaling, and optimising intelligent mapping solutions using multi-modal AI. As an AI/LLM Data Scientist, you will work on cutting-edge applications ranging from search and recommendation systems to traffic modelling, routing, and map intelligence.

You will research, architect, train, fine-tune, and deploy advanced models—including large language models (LLMs), computer vision models, and agentic AI systems—to improve map accuracy, intelligence, and user experiences across Grab’s platform ecosystem.

Key Responsibilities

• Identify research or product opportunity areas and translate them into technical problem statements with measurable outcomes.
• Own end-to-end development of small to mid-scale products or models—from experimentation to production rollout.
• Develop and optimize machine learning and deep learning models including LLMs, multi-modal models, and generative AI systems.
• Fine-tune and evaluate foundation models (GPT, Llama, Qwen, etc.) using supervised, reinforcement learning, or retrieval-augmented approaches.
• Architect agentic AI workflows using frameworks such as LangChain, LlamaIndex, or OpenAI function calling.
• Stay up to date with advancements in NLP, LLMs, agentic systems, recommendation engines, and computer vision.

Skills and Qualifications

• Master’s Degree in Computer Science, Electrical/Computer Engineering, Operations Research, or related field.
• Hands-on experience with deep learning and LLMs including fine-tuning, evaluation, and adaptation for downstream tasks.
• At least 1+ year of experience deploying LLMs or agentic AI systems in production environments (TorchServe, Triton, Ray Serve, etc.).
• Strong understanding of model optimisation techniques like quantization, compression, and inference-time acceleration.