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Ziwen Shen
Junior AI/ML Engineer specializing in machine learning and applied research
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Machine learning/AI engineer focused on agentic product experiences, including a parts-finding assistant and other AI-driven tools. Has worked on reinforcement learning projects, agent state management, and making AI understandable for non-technical users through visuals and simplified explanations.
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Key Strengths
- built AI agents for parts search/use-case assistance
- experience designing stateful conversational product flows
- hands-on ML engineering with reinforcement learning algorithms
- research-driven approach using papers to implement algorithms
- focus on reliability factors like accuracy, memory, and response time
- ability to simplify AI concepts for non-technical audiences with visuals
- Deployed a Multi-Agent RL model to production serving 30+ firms/enterprise clients
- Bridged research code to production systems via scalable data pipelines and API specs
- Resolved production environment mismatch and latency using Docker containerization and batched inference
- Stabilized MARL system for dynamic firm entry/exit by refactoring for variable-length inputs and refining reward functions
- Full-stack incident debugging across network symptoms, hardware metrics, and software root cause (memory exhaustion/GC)
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