Meta · Walmart Labs · Deep Learning · LLMs · Scalable ML Systems
Building production ML at scale — from Transformer-based forecasting and Foundation Model fine-tuning to recommendation systems driving revenue across billions of ad auctions.
I'm a Machine Learning Scientist with a Ph.D. from the University of Arizona, with 6+ years of industry experience at Meta and Walmart Labs building and shipping large-scale ML systems.
My work spans deep learning model development, Foundation Model fine-tuning, Transformer-based time series forecasting, auction-based recommendation systems, and operational ML — handling billions of data points daily in production.
I bridge the gap between cutting-edge research and real business impact: cutting inference costs, lifting revenue, and accelerating model iteration across some of the world's largest technology companies.
A full-spectrum ML toolkit — from model development to cloud deployment and big-data pipelines.
Production ML systems serving millions of users at FAANG and top-tier tech.
Applied ML projects spanning computer vision, NLP, time series, and recommendation systems.
Ensemble of CNN architectures to classify abnormal vs. normal X-rays from the MURA musculoskeletal dataset. Different CNN backbones detect diverse fracture types, boosting ensemble accuracy over single models.
View on GitHubMulti-class sentiment analysis of tweet data using fine-tuned large-case BERT. Outperforms RNN, LSTM, and GRU baselines, demonstrating the power of Transformer-based pre-training for NLP tasks.
View on GitHubPanel data forecasting with cross-sectional and temporal variation. Predictive models for user mission-completion in a health app — combining time-series and cross-sectional analysis techniques.
View on GitHubDeep learning model using embedding projectors to predict article upvote/downvote status from title text. Includes Tableau visualizations to surface key dataset characteristics.
View on GitHubSalary quartile classification on highly imbalanced data. Explored SMOTE, up/downsampling, and ensemble ML models. Evaluated via F1 and ROC-AUC to select the best-performing approach.
View on GitHubML solution for detecting fraudulent credit card transactions on a highly imbalanced dataset (0.17% fraud rate). Handles class imbalance with appropriate evaluation metrics and model selection.
View on GitHubPeer-reviewed research in applied ML, Bayesian methods, and industrial engineering.
Open to senior MLE roles, applied ML research collaborations, and speaking on ML at scale.