Job Description

Design and build the systems that generate longitudinally coherent synthetic environments for agent training and evaluation, including persona modeling, task generators, and verifiable ground truth. Build and maintain synthesis models that generate realistic replacement values at very large scale, preserving format, statistical distribution, and semantic consistency so de-identified data stays useful downstream. Train and improve the NER models behind entity detection, driving accuracy and recall across free text, structured fields, and mixed enterprise data at scale. Build evaluation infrastructure that grades agent outcomes, not just traces, and produces real discrimination between frontier models on real tasks. Fine-tune and evaluate open-weight models on Tonic-generated data, and turn benchmark results into product and research direction. Expand coverage into new domains, languages, and entity types, and handle the long tail of formats and edge cases that real customer data throws off. Own model evaluation across the board: precision and recall on detection, utility preservation on synthesis, and outcome-level grading for agents. Optimize inference so models run efficiently on large volumes of sensitive data inside customer environments. Partner directly with frontier labs and enterprise ML team to turn hard data problems into shipped model improvements. Set technical direction for a small, senior team and raise the bar on rigor, reproducibility, and shipping.