AI Research Engineer (Bangkok Based)
On-siteBangkok, Bangkok, Thailand
Job Summary
Conduct original research in large language models, NLP, computer vision, or agentic systems by designing rigorous experiments, ablation studies, and benchmark evaluations to validate new methods. Develop novel algorithms and train large-scale deep learning models using PyTorch, optimizing performance through quantization, pruning, and efficient attention mechanisms while building distributed training infrastructure across GPU/TPU clusters. Translate research outcomes into production-ready features by designing RAG pipelines with vector databases, architecting LLM-powered agents with tool calling and memory, and integrating AI capabilities into customer-facing platforms via APIs. Curate high-quality datasets, maintain scalable ML infrastructure using Docker and Kubernetes, and monitor deployed models through automated retraining pipelines and observability tools. Author technical papers, present at conferences, and contribute to open-source projects to communicate findings to technical and non-technical audiences.
Required Qualifications
- Master's or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, Electrical Engineering or a related quantitative field
- 3+ years of hands-on experience in AI/ML research or research engineering
- demonstrated ability to design experiments, analyse results and iterate on methods
- At least one first-author or co-author publication at a recognised venue (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, AAAI, or equivalent), or equivalent demonstrated research output (patents, technical reports, significant open-source contributions)
- Strong proficiency with Python and modern deep learning frameworks (PyTorch, JAX, or TensorFlow)
- solid understanding of model architectures (Transformers, diffusion models, GNNs) and training techniques (RLHF, DPO, SFT, pre-training)
- Ability to write clean, maintainable, production-quality code
- familiarity with software engineering best practices (version control, code review, testing, CI/CD)
- Strong grounding in linear algebra, probability, statistics, optimisation and information theory
Desired Qualifications
- Experience designing agentic architectures (tool/function calling, planning, memory, multi-agent orchestration via frameworks such as LangChain, LlamaIndex, AutoGen or CrewAI)
- Hands-on experience with retrieval-augmented generation pipelines, embedding models, hybrid search and vector databases
- Experience with large-scale distributed training across multi-GPU/TPU environments (DeepSpeed, FSDP, Megatron-LM or similar)
- Working knowledge of cloud platforms (AWS, GCP or Azure) and ML operations tooling (MLflow, W&B, Kubeflow)
- Active contributions to well-known AI/ML open-source projects or libraries
- Excellent written and verbal communication skills
- ability to distill complex research into clear recommendations for diverse stakeholders
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