Master’s or PhD in Computer Science, Artificial Intelligence, Data Science, or related fields.
Strong experience with industrial large model reinforcement learning systems.
Minimum 3 years of experience managing research-oriented teams, defining business objectives, coordinating resources, and optimizing talent development pipelines.
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Build core LLM competitiveness in long sequences, agents, and strong reasoning, delivering breakthroughs in new architectures and training algorithms
Design and build AI Agent features — RAG, tool calling, multi-agent collaboration, and orchestration across multiple models — from concept through production
Partner with Product Management to translate business goals into scalable, agent-based engineering solutions
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Knowledge Base Architecture: Build and iterate on domain knowledge bases, codifying professional judgment criteria, standard analysis protocols, and industry know-how into governable, reusable digital assets.
AI Skill Packaging: Lead the design and packaging of semiconductor-specific AI Skills, establishing professional standards for intelligent retrieval, technical reasoning, and automated technical report generation.
Output Validation: Validate AI-generated outputs to ensure analytical results are technically rigorous, industrially feasible, and consistent with global semiconductor technology trends.
...
Build core LLM competitiveness in long sequences, agents, and strong reasoning, delivering breakthroughs in new architectures and training algorithms
Design and build AI Agent features — RAG, tool calling, multi-agent collaboration, and orchestration across multiple models — from concept through production
Partner with Product Management to translate business goals into scalable, agent-based engineering solutions
...
Master’s or PhD in Computer Science, Artificial Intelligence, Data Science, or related fields.
Strong experience with industrial large model reinforcement learning systems.
Minimum 3 years of experience managing research-oriented teams, defining business objectives, coordinating resources, and optimizing talent development pipelines.
...
Use simulation and digital twins to model production flows, equipment behavior, and system constraints, and to safely test and refine agent strategies before deployment.
Integrate predictive models (e.g. for demand, machine health, cycle time, yield) into expert systems to support proactive and prescriptive decisions.
Collaborate closely with process owners, planners, and equipment engineers to translate operational knowledge into rules, heuristics, and agent objectives.
...
Use simulation and digital twins to model production flows, equipment behavior, and system constraints, and to safely test and refine agent strategies before deployment.
Integrate predictive models (e.g. for demand, machine health, cycle time, yield) into expert systems to support proactive and prescriptive decisions.
Collaborate closely with process owners, planners, and equipment engineers to translate operational knowledge into rules, heuristics, and agent objectives.
...