Machine Learning Engineer at Kudo

Beijing, Beijing, China -

Full Time

Start Date

Immediate

Expiry Date

21 Apr, 26

Salary

0.0

Posted On

21 Jan, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Deep Learning, Natural Language Processing, Python, Data Pipelines, Prompt Engineering, LLMs, Computer Vision, Information Retrieval, Cloud Environments, Data Science, AI, Collaboration, Experimentation, Model Evaluation, Programming

Industry

Software Development

Description

Company Description About Grab and Our Workplace Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility. Job Description 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. Qualifications Requirements Master's or Bachelor's degree in Computer Science, Data Science, AI/ML, or a related field Hands-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 plus Good 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. 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.

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Responsibilities

The role involves collaborating with senior data scientists to adapt and fine-tune LLMs for map-related tasks and designing AI workflows that integrate LLMs with various tools. Additionally, the engineer will implement data pipelines and contribute to prototyping retrieval-augmented generation pipelines.