You will set the scientific direction for customer-grounded quality: define what "good" means, assess whether eval portfolios represent real needs, diagnose model and agent failures, and convert evidence into reusable evals, RLEs, reward signals, and post-training priorities. Translate user research, enterprise customer feedback, DSAT, and production incidents into evaluation and post-training priorities, then lead cross-team creation of reusable evaluation, regression, RLE, and post-training assets for the highest-value workflows and failure patterns.