AI Research Intern at NVIDIA focusing on multi - modal AI and vision - language model development. Collaborating with engineers to advance cutting - edge machine learning research in Vietnam.
About the role
- Lead end-to-end applied research projects: define experiments, implement prototypes, run evaluations, and hand off reproducible artifacts to product/engineering teams.
- Own at least one deep technical domain (e.g., embedding model design & evaluation, context modeling for agentic systems, on-line or continuous adaptation / fine-tuning pipelines) while contributing across other areas.
- Translate research papers and state-of-the-art approaches into pragmatic, production-aware prototypes and AB-testable experiments.
- Build reliable evaluation pipelines and datasets (offline metrics + human evaluation) and document failures/lessons.
- Mentor and uplevel ML engineering practices across Mercari – techniques, model design, code quality, reproducible experiments, and sound evaluation.
- Collaborate closely with product managers, designers and platform engineers to scope and prioritize research that can move into product, as well as inspire and shape product vision.
Requirements
- Either one of the following:
- 5+ years building state-of-the-art ML systems in industry
- PhD and 1+ years industry experience
- Strong software engineering skills (Python, PyTorch or TF).
- Demonstrated depth and recent experience in at least one of our core focus areas:
- Semantic Understanding, e.g. multimodal-embeddings, representation learning, or latent variable modelling
- Contextual Intelligence, e.g. graph- or memory-augmented systems, retrieval-augmented generation, or agentic architectures that model user or item context
- Continuous Learning, e.g. preference learning (DPO/variants), reinforcement learning from user feedback (RLHF/RLAIF), or online/continual fine-tuning pipelines
- Strong track record of shipping prototypes or models end-to-end (not just research code).
- Ability to design experiments: dataset creation, metrics, human eval, and interpretable analysis.
- Excellent communication: explain technical tradeoffs to product and engineering audiences.
- Product & UX sense: willingness to frame user problems, success metrics, and UX trade‑offs with PM/Design.
- Comfortable working autonomously in a small, high-focus team that protects flow.
- Preferred Experience/Skills
- Evidence of autonomous research‑to‑product impact (open‑source libraries, internal platforms, or papers with code).
- Previous product or e-commerce experience (valuable but not required).
- Language
- English: Independent (CEFR - B2) Required
- Japanese: Independent (CEFR - B2) Optional
- For details about CEFR, see here.
Job title
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