Research Track

Research Track

Since 2019, the AI Residency Program (Research-based) has trained 70 brilliant young talents and had 33 papers accepted and published at Top-tier conferences such as CVPR, NeurIPS, AAAI, ICML, ICLR, EMNLP, NAACL. The program also records 58 Ph.D. scholarships across the world’s top 20 Computer Science universities.

Throughout the two-year program, residents have the opportunity to contribute to every aspect of a research project: from idea inception; methodology formulation and exploration; experiment design and implementation; result interpretation; to reporting and presentation at top-tier AI conferences.

Benefits

  • Up to $20.000/year training package.
  • Access to world-class mentorship.
  • Benefit from a rich set of courses in Machine Learning, Maths, Computer Vision and Robotics, Natural Language Processing and Speech, etc.
  • Connect with globally recognized AI Research Scientists & Engineers.
  • Benefit from a competitive compensation package.

Requirements

  • Current penultimate or final year students, recent graduates in a CS program (or equivalence) in a University in Vietnam or abroad.
  • A minimum GPA of 3.2/4.0 or 8.0/10.
  • A motivated individual with a great interest in research.
  • Good mathematics or coding skills.
  • Intermediate English learners.

Engineer Track

Engineering Track

AI Residency Program: Engineering Track is a 6-month training program designed for fresh graduates. Upon successful completion, the residents will be eligible to join the AI engineering and product team at VinAI. The program covers theoretical AI knowledge, hands-on coding skills with practical experience of working on real projects.

Residents are exposed to intensive courses in machine learning, computer vision and natural language processing, led by seasoned engineers and researchers, and provided with direct mentorship from a VinAI Applied Team member.

The program ensures all residents acquire a marketable skill set by offering them three months of product applications and three months of real-world projects.

Benefits

  • Competitive compensation (up to $20.000/year for training package).
  • Gain industry-relevant AI knowledge.
  • Connect with top minds from VinAI.
  • Opportunity to publish research outputs at top-tier AI venues.
  • Gain hands-on experience with products, especially from Vingroup ecosystem and world-class projects.
  • Involved in 1:1 mentorship with world-class researchers.

Requirements

  • Current penultimate or final year students, recent graduates in a Computer Science program (or equivalence) in a University in Vietnam or abroad.
  • A minimum GPA of 3.2/4.0 or 8.0/10.
  • Good knowledge in either mathematics or coding skills or both. Prefer candidates with proven records (e.g., previous research experiences at local university, participation in international/national competitions in science and programming, popular GitHub repos of implementation of machine-learning models, completion of basic online AI open course).

Engineer Track

Engineering Track

AI Residency Program: Engineering Track is a 6-month training program designed for fresh graduates. Upon successful completion, the residents will be eligible to join the AI engineering and product team at VinAI. The program covers theoretical AI knowledge, hands-on coding skills with practical experience of working on real projects.

Residents are exposed to intensive courses in machine learning, computer vision and natural language processing, led by seasoned engineers and researchers, and provided with direct mentorship from a VinAI Applied Team member.

The program ensures all residents acquire a marketable skill set by offering them three months of product applications and three months of real-world projects.

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ALUMNI
NETWORK

Thao Nguyen

AI Engineer
VinAI

Manh Luong

AI Engineer
VinAI

Tuan Nguyen

AI Engineer
VinAI

Duong Le

AI Engineer
VinAI

Chuong Huynh

PhD student
University of Maryland

Tung Nguyen

PhD student
University of California, Los Angeles

Khai Nguyen

PhD student
University of Texas at Austin

Khang Le

PhD student
University of Texas at Austin

Anh Do

PhD student
Johns Hopkins University

Khiem Pham

PhD student
Cornell University

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