• Architect and implement high-throughput, deterministic data sampling systems capable of feeding distributed training clusters at frontier-model scale. • Design and execute dynamic curriculum learning strategies, creating systems that automatically adjust data distributions (text vs. multimodal) during training to improve model stability and reasoning capabilities. • Engineer the logic for serializing Reddit’s complex conversational trees (threads, subreddits, cross-posts) into optimal training contexts, developing topological data processing strategies that preserve semantic relationships for model understanding. • Formulate and validate statistical hypotheses regarding data mixtures, leveraging advanced sampling theory to minimize bias and maximize token quality. • Design the "Safety-First" ingestion layer: Build automated pipelines for PII redaction, toxicity signals, and quality deduplication upstream of training, working closely with our Safety and Moderation Engineering counterparts. • Bridge the gap between research and engineering by translating theoretical sampling insights into robust, low-latency production infrastructure. • Mentor senior engineers and researchers on system design, numerical correctness, and performance optimization within distributed Python/Rust environments.