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Annotate and manage training/test datasets, run model training, evaluate model performance, perform manual QA and error analysis, and refine annotation guidelines for NLP/ML projects.

Updates machine learning training and test datasets, refines annotation guidelines, initiates model training, and evaluates model performance. The role annotates synthetic text and image data, conducts manual quality analysis, identifies error patterns, reports anomalies, and produces findings on utterance quality, LLM evaluation, ASR bugs, and customer pain points. It also applies basic NLP and linguistic knowledge to ensure data accuracy and consistency.

Lead linguistic quality for an account: edit and proofread localized content, perform translations and reviews, define quality expectations, run LQAs and RCAs, liaise with Apple SMEs, manage linguistic assets, coach and onboard linguists, track KPIs, and drive corrective actions and arbitration.

Leads an independent quality and research-partnership function supporting crowd-based AI training data operations. Oversees onsite Site Program Leads, maintains separation from delivery teams, partners directly with research stakeholders, translates research needs into crowd data projects, and supports project stand-up. Owns capacity planning, utilization, governance, talent pipelines, escalation protocols, and client engagement while serving as a senior individual contributor.