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Ziwen Shen
Junior Machine Learning Engineer specializing in computer vision, reinforcement learning, and PINNs
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About
ML/Simulation engineer who productionized a Multi-Agent Reinforcement Learning system for 30+ firms at Belt and Road Big Data Company, integrating research code into an enterprise backend via Dockerized deployment and scalable data pipelines on GCP/Vertex AI. Demonstrated strong production debugging by tracing apparent network timeouts to hardware memory exhaustion caused by software state-history garbage collection issues, and built custom reward functions to model complex market dynamics (entry/exit, pricing).
Experience
Education
Key Strengths
- 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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