About the role
About Turing Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises looking to deploy advanced AI systems. Turing accelerates frontier research with high-quality data, specialized talent, and training pipelines that advance thinking, reasoning, coding, multimodality, and STEM. For enterprises, Turing builds proprietary intelligence systems that integrate AI into mission-critical workflows, unlock transformative outcomes, and drive lasting competitive advantage. Recognized by Forbes, The Information, and Fast Company among the world’s top innovators, Turing’s leadership team includes AI technologists from Meta, Google, Microsoft, Apple, Amazon, McKinsey, Bain, Stanford, Caltech, and MIT. Learn more at www.turing.com Position Overview We are looking for a seasoned Delivery Manager (8+ years of experience) to lead large-scale data annotation and curation projects in support of cutting-edge LLM and multimodal AI research. You will oversee end-to-end delivery for projects with 100+ team members, ensure world-class data quality, and drive process and tooling innovation in collaboration with AI researchers and cross-functional teams. This role is ideal for someone who combines deep technical expertise, proven leadership in managing large teams, and a passion for advancing AI/ML innovation. Given that our key customers operate in the data analytics, data engineering, and Business Intelligence (BI) space, the ideal candidate will bring hands-on leadership experience with platforms such as Databricks and Snowflake, enabling them to deeply understand customer domains, speak their language, and deliver annotation outcomes that are contextually relevant and technically sound. Primary
Responsibilities 1. Team Leadership & Development Lead, mentor, and grow large-scale teams of annotators and trainers. Drive performance management, career development, and targeted training. Foster a culture of excellence, ownership, and continuous learning. Design and implement motivation strategies (including gamification) to boost engagement and reward top performers. 2. Project Management & Coordination Define and manage project scope, objectives, timelines, and quality metrics. Align with cross-functional stakeholders on goals and deliverables. Proactively identify and resolve delivery risks, blockers, and dependencies. Provide clear and consistent reporting on progress, challenges, and solutions. 3. Data Quality & Integrity Own quality control and data integrity across the annotation lifecycle. Analyze datasets to identify trends, anomalies, and insights for improvement. Implement best practices for annotation, evaluation, and data curation. Share learnings and continuously improve quality processes. 4. Technical Collaboration & Innovation Partner with engineering and research teams to optimize internal tooling. Stay ahead of emerging trends in AI, ML, and multimodal processing. Champion innovations that reduce task completion times and improve efficiency. Maintain clear and scalable documentation for processes and training. 5. Customer & Domain Engagement Serve as a domain-knowledgeable point of contact for customers operating in data engineering, analytics, and BI environments. Leverage hands-on experience with platforms like Databricks and Snowflake to understand customer data workflows, terminology, and quality expectations. Translate customer-specific data engineering and BI contexts into clear, actionable annotation guidelines and quality benchmarks for delivery teams. Collaborate with customers to define annotation taxonomies and evaluation criteria that align with their data pipeline and analytics use cases. Required Skills &
