AI Research Scientist

Description：
As an AI Research Scientist, you will work closely with engineering teams to drive innovation in natural language processing and integrate cutting-edge AI technologies into company products. Your role will involve developing novel AI/NLP solutions to support the global expansion and game business of the G123 platform, with a key focus on practical applications and innovative thinking.
Responsibilities：
Collaborate with engineering teams to integrate AI technologies into existing products, driving functionality optimization and enhanced user experience.
Research, design, and develop innovative AI systems or algorithm prototypes, and validate their effectiveness in practical scenarios.
Lead or participate in requirement analysis, solution design, and implementation of key projects, ensuring on-time delivery and high-quality outcomes.
Continuously monitor industry trends and apply the latest LLM and deep learning research results to real-world problems.
Draft technical documentation and communicate effectively with cross-functional teams to facilitate project progress.
Basic Qualifications：
Master’s degree or above in Computer Science, Mathematics, Computational Linguistics, or related disciplines.
At least 3 years of research or industry experience in AI fields such as machine translation, text generation, or dialog systems.
Strong practical experience and knowledge in large language models (LLMs) and their applications.
Proficiency with mainstream deep learning frameworks such as TensorFlow or PyTorch, and solid programming skills (e.g., Python).
Demonstrated data analysis and algorithm development abilities, with a passion for AI/NLP and a spirit of innovation.
Preferred Qualifications：
PhD degree is a strong plus.
Publications in top-tier AI conferences or journals.
Familiarity with mainstream NLP tools such as spaCy and NLTK.
Experience with deploying models on cloud platforms like AWS, Google Cloud, or Azure.
Ability to read and comprehend academic literature in English; multilingual work background is a plus, as is experience working on large-scale complex systems or with multinational teams.
What success looks like in the role:
1. Production-grade deployment of agents in real business workflows
• Able to land agents into real engineering systems (integration, observability, reliability, security, evaluation).
• Designed to replace parts of traditional engineering operations, delivering measurable labor savings and cost reduction.
2. Multimodal model capability for the gaming vertical
• Build/finetune multimodal models for games, including:
• Game QA simulation (automated QA via agents)
• Playing games (game-playing agents / interactive policies)
• Gaming foundation model capability (a strong base model specialized for game understanding
Show more Show less