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Lead end-to-end technical programs for ML infrastructure (inference, serving, efficiency, endpoints). Coordinate cross-functional teams, prioritize overlapping projects, manage timelines, budgets and scope, drive incident management and continuous improvement, and communicate status to engineering and leadership.

Build and own tooling and data pipelines for synthesis, annotation, validation, and analysis of real and synthetic data to support model training and evaluation. Collaborate with researchers and engineers to align data infrastructure with experiments, maintain documented standards, and develop frameworks to identify coverage gaps and improve data quality.

As a Software Engineering Intern, you will contribute to building features for user-facing products, APIs, and scalable services for machine learning. Responsibilities include crafting code, designing data pipelines, implementing tools, and enhancing technical documentation.

This role involves building and scaling compliance programs, translating security and regulatory requirements into scalable solutions, and collaborating across teams, specifically in AI governance and technical compliance automation.