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.
Industry ML projects from Meta and Walmart Labs spanning forecasting, recommendation systems, inference optimization, and MLOps.
Built a Transformer-based time series forecasting model for multi-horizon demand forecasting across product categories. Leveraged foundation model architectures with few-shot adaptation, achieving a 30% reduction in MAPE versus previous LSTM baselines and cutting inventory costs across Walmart's supply chain.
Internal ProjectDesigned and deployed a DeepFM-based recommendation model for auction-style ad placement, combining factorization machines with deep neural networks to capture high-order feature interactions. End-to-end production deployment drove a 15% revenue uplift in sponsored product placements.
Internal ProjectLed end-to-end optimization of large foundation model inference pipelines using quantization, model distillation, and batching strategies on GCP. Reduced inference latency by 60% while maintaining model accuracy, enabling real-time serving at scale for recommendation workloads.
Internal ProjectDeveloped a dynamic floor pricing system for programmatic advertising using reinforcement learning and bandit algorithms to adaptively set minimum bid prices per auction. Increased ad display win rate by +5% while maximizing revenue per impression across demand partners.
Internal ProjectBuilt a comprehensive ML observability platform at Meta to track model health, feature drift, and business ROI across production models. Automated alerting pipelines integrated with Airflow and internal dashboards, enabling proactive model retraining and reducing incident response time for ML degradations.
Internal ProjectDesigned and deployed a forecasting system to predict AI tool adoption and productivity impact across engineering teams at Meta. Combined time series models with causal inference to isolate tool-driven productivity gains, influencing 25% improvement in developer productivity and informing AI investment strategy.
Internal ProjectPeer-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.