AI Agent Algorithm Engineer
Job Description
Job Description:
- 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.
Requirements:
- Master's degree or above in Artificial Intelligence, Computer Science, Mathematics, or a related field.
- At least 2 years of full-time industry experience building and deploying production multi-agent LLM systems (task planning, orchestration, tool calling).
- Hands-on experience fine-tuning LLMs via SFT and DPO, combined with hands-on experience building and optimizing RAG/retrieval systems (recall, ranking, embedding fine-tuning).
- Good programming skills proficient in Python
- Good problem solving 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 LangGraph, Google Agent Development Kit, OWL, or AutoGen.
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Key Skills
Embedding fine-tuning
Multi-agent collaboration
Recall ranking
RAG retrieval systems
Memory architectures
Task planning
Multi-turn dialogue management
Context management
Long-text processing
Multi-document synthesis
Quantitative evaluation systems
Tool calling
RL reward models
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