Senior AI Research Scientist

Description：

As a Senior AI Research Scientist, you will work closely with engineering teams to drive innovation in natural language processing and integrate cutting-edge NLP 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.

Requirements：

• PHD degree or above in Computer Science, Mathematics, Computational Linguistics, or related disciplines.
• At least 5 years of research or industry experience in NLP 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：

• 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