Summary
Jinmei Zhang is a machine learning engineer in the San Francisco Bay Area with 15 years of experience applying rigorous, research-driven approaches to production ML systems. She has moved models from prototype to production across healthcare claims anomaly detection, time-series forecasting, and NLP-driven brand resolution, combining feature engineering, hyperparameter tuning, and model interpretability. Her background in computational chemistry and high-performance scientific computing informs her strength in algorithmic optimization and memory-efficient implementations, proven by earlier work reducing computational cost in quantum chemistry. At Amplitude and Anomaly she shipped monitoring and anomaly-detection pipelines using Airflow, SageMaker, and TensorFlow, and at CircleUp she integrated fine-tuned BERT and optimized Spark workloads for scale. Jinmei is comfortable bridging research and engineering—experimenting with novel models (TCNs, LSTMs, CNNs, XGBoost) while ensuring production reliability and measurable business impact.
15 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (PhD) Physical Chemistry, Doctor of Philosophy (PhD) Physical Chemistry at Virginia Tech
English, Chinese