Augustus Hong

Led backend and ML delivery of an in-vehicle conversational assistant, building agent/tool orchestration, safety-gated execution, telemetry pipelines, and optimized multi-node training/inference to meet real-time latency and compliance targets.

Built DLRM-style ads ranking models and productionized distributed PyTorch training and inference, improving ranking quality via feature/embedding tuning and gating launches with regression coverage and online A/B tests.

Developed a real-time Agent Assist product with ML-backed intent/moment detection, built orchestration and low-latency inference paths, and shipped UI and mobile surfaces while managing deployment and observability.

Designed and implemented high-accuracy NLP classifiers, led strategies for high-quality training data, and built dashboards to track product metrics and campaign performance.

Built search and recommendation systems, implemented API and backend data storage components, and ran A/B experiments to measure engagement lift for partner integrations.

Built user feature modeling and analytics backends for high-throughput real-time data streams, driving product re-engagement and mining business insights from large datasets.

Developed a full-stack web application and iOS client to improve trip planning, contributing front-end and back-end features for product demonstrations.

Developed software infrastructure to optimize simulation performance for CPU core verification, improving verification throughput and tooling reliability.

Master of Science in Artificial Intelligence completed at Stanford University with coursework and projects focused on machine learning and AI systems.

Bachelor of Science in Computer Science from Purdue University with foundational coursework in algorithms, systems, and software engineering.