• Architect and maintain the "Reddit Benchmark" evaluation suite: A comprehensive harness that rigorously tests model capabilities across Safety, Reasoning, and Reddit-specific knowledge (slang, norms). • Build scalable SFT (Supervised Fine-Tuning) pipelines: Implement efficient, distributed training loops for instruction tuning, converting raw base models into helpful assistants. • Develop Model-as-a-Judge systems: Engineer automated evaluation pipelines using strong models (e.g., GPT-5, Nova, Claude) to grade the outputs of our internal models, enabling rapid iteration cycles. • Execute Synthetic Data generation strategies: Create and curate high-quality instruction sets to improve model generalization where human data is scarce. • Collaborate with Safety Engineering: Translate high-level safety policies into concrete evaluation metrics and unit tests that run in our CI/CD pipelines. • Debug post-training instability: Dive deep into loss curves and evaluation logs to identify when fine-tuning is causing alignment tax or capability degradation.