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Salary: USD 200,000 - 300,000 / annual
Simile has built the first AI simulation of society, powered by generative agents modeled on real humans. The company is developing a foundation model to predict human behavior at scale, backed by $100M in funding from Index Ventures and leading AI researchers including Andrej Karpathy and Fei-Fei Li.
As a Data Operations professional, you will own the complete data lifecycle at Simile—from sourcing third-party datasets to managing first-party data collection. You sit upstream of research, engineering, and customer deployments, making critical decisions about which populations can be credibly simulated and which datasets are worth acquiring.
Key responsibilities include: expanding geographic and demographic coverage by identifying and securing datasets that enable new simulation domains; running Simile's data collection supply chain, including vendor management, incentive design, and quality assurance; structuring data licensing agreements optimized for foundation model training and derivative use cases; building feedback loops that connect research and deployment needs to sourcing strategy; defending data fidelity by setting standards for sample composition and response quality; and ensuring compliance with privacy, consent, and usage rights for enterprise and government partners.
You will work in a small, high-ownership team with direct access to researchers and customers. The role requires comfort with ambiguity, strong negotiation skills (especially when lacking leverage), and the ability to manage multiple concurrent initiatives while maintaining clear documentation. You should have intuition for data quality, understand sample selection and bias issues, and be willing to reject impressive-looking datasets that don't meet credibility standards.
Ideal candidates have consulting or investing experience tackling complex problems, procurement background with vendor contract negotiation, or technical fluency to automate workflows independently.
