Agent Application Algorithm Engineer

Optimize AIGC content generation quality by advancing multimodal understanding of images, audio, and video; improving image and video generation pipelines; and addressing generation consistency issues. Build core Agent logic, including but not limited to task planning and orchestration, tool calling, multi-turn dialogue management, memory, RAG, context engineering, and multi-agent collaboration.

Lead Continuous Pre-training and Post-training for vertical domains and business scenarios, including building high-quality datasets and data pipelines, designing RL reward models, improving instruction following and reasoning capabilities, task completion, role-playing, anthropomorphic and personalized dialogue, proactive/reactive immersive multimodal conversation experiences, and enhancing the model's IQ and EQ.

Build long-term and short-term memory architectures, addressing issues such as forgetting and attention dispersion in long contexts, and improving immersion and consistency in long-term user interactions. Build multimodal RAG systems, including development and optimization of key modules such as recall, ranking, long-text processing, and multi-document synthesis.

Develop the Agent's tool layer, integrating external APIs and MCP such as search, code interpreters, browsers, sandboxes, and third-party services. Design and tune prompts and context management, with tailored optimization for different product requirements. Design scientifically rigorous quantitative evaluation systems and plans aligned with product requirements; continuously monitor product metrics and provide guidance for Agent and model optimization.

Explore innovative AI applications. A minimum of 2 years of experience in computer vision, multimodal AI, or generative AI, with hands-on experience delivering production projects. Familiarity with the principles and applications of mainstream vision-language models (VLMs), such as Qwen-VL, InternVL, GPT, and Gemini series, as well as diffusion and video generation models.

Master's degree or above in Artificial Intelligence, Computer Science, Mathematics, or a related field. Strong programming skills; proficient in Py. Torch; familiar with distributed training frameworks such as Deep. Speed and Megatron. Strong experience with techniques and principles of LLM training/inference, including but not limited to data synthesis and filtering, model training optimization, prompt engineering, evaluation, deployment, and prototype development.

Strong problem analysis and resolution skills; sustained interest and curiosity in frontier AI technologies and applications; strong self-drive; able to collaborate closely with teams to drive a full closed loop from research to deployment. Good development experience with Agent frameworks such as Lang. Graph, Google Agent Development Kit, or OWL.

Strong engineering capabilities; familiar with AI development tools such as Cursor and Claude Code; proactive mindset for improving efficiency. Prior project experience in areas such as Agentic reinforcement learning, virtual character generation, multimodal interaction, personality modeling, or memory will be a strong plus.