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Yuxiang Xie

@yuxiangxie

Staff AI/ML Engineer at OpenAI building LLM evaluation platforms and generative AI applications for analytics and knowledge retrieval.

United States

Message

At OpenAI, I build generative AI applications for analytics, knowledge retrieval, and decision support, along with reusable RAG and agent components exposed through FastAPI services. I also developed model-evaluation infrastructure spanning reasoning, factual accuracy, tool use, safety, and multimodal understanding.

I co-authored alignment research on model monitorability and established GenAIOps workflows for versioning prompts, models, datasets, and evaluation suites. My work also includes a B2B revenue-forecasting framework and machine-learning job-function classifiers.

At OpenAI, I engineered fingerprint data pipelines and recognition models, and developed synthetic fingerprint generation and liveness detection. On benchmark evaluations, the recognition models achieved approximately 97.9% TAR at 0.01% FAR, while large-gallery identification reached 94%+ Rank-1.

Experience

Work history, roles, and key accomplishments

Current

Staff AI/ML Engineer

Current

OpenAI

Sep 2025 - Present (1 year 2 months)

Architected generative AI applications and evaluation infrastructure, built RAG and agent workflows, and led biometric AI projects including fingerprint recognition and liveness detection.

Python fastAPI RAG LLM VLM PyTorch TensorFlow Gan VAE OpenCV PostgreSQL AWS Docker Kubernetes MLOps GenAIOps

Staff Machine Learning Engineer

Robinhood

Feb 2025 - Sep 2025 (7 months)

Architected causal inference and experimentation platform, developed variance reduction and heterogeneous treatment effect frameworks, and established launch-readiness criteria.

Python Causal Inference A B Testing HTE Statistical Testing Experimentation Variance Reduction Data Analysis

Senior Machine Learning Engineer

Snap Inc.

Jul 2019 - Feb 2025 (5 years 7 months)

Led forecasting and retention modeling, integrated causal inference and experimentation methods, and developed segmentation and clustering models for user behavior analysis.

Python Forecasting Time Series Causal Inference A B Testing Segmentation Clustering HTE FDR Statistical Modeling

Research Associate

University of Washington

Sep 2014 - Jun 2019 (4 years 9 months)

Conducted doctoral research in biostatistics on sparse high-dimensional learning, variable selection, and heterogeneous treatment effects, developing statistical software toolkits.

Python R Statistical Modeling Causal Inference Firepad FDR NMF Simulation

Research Intern

Snap Inc.

Sep 2017 - Dec 2017 (3 months)

Designed and implemented a Python-based HTE toolkit for analyzing online controlled experiments, and co-authored a paper presented at KDD 2018.

Python HTE A B Testing Hypothesis Testing FDR Simulation Experimentation

Education

Degrees, certifications, and relevant coursework

University of Washington

Ph.D., Biostatistics

2014 - 2019

Ph.D. in Biostatistics from the University of Washington, with research focus on sparse high-dimensional learning, variable selection, and heterogeneous treatment effects.