Client Director, Frontier Data

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

Overview

We are seeking a seasoned techno-functional leader to drive the development and execution of large-scale LLM training programs. This leader would partner with our clients (leading LLM labs) research teams to:

• Identify opportunities for building training datasets to improve model capabilities and performance
• Generate these datasets with high quality and speed
• Build automation tools and processes for scalability
• Deliver the datasets so that they are easily usable by our clients

Key Responsibilities

Operational Leadership & Performance Management

• Lead and scale global delivery teams of 100+, distributed across functions, regions, and levels (ICs, leads, and managers)
• Implement performance management systems that go beyond managerial reporting using data-driven metrics, tools, and products to assess productivity, quality, and output consistency
• Build strong operational structures that allow for transparency, accountability, and early detection of underperformance
• Partner with cross-functional leads to optimize workflows and improve internal tool adoption for delivery efficiency

Data Quality & Scripting-Driven Automation

• Own the quality, accuracy, and scalability of data generated for LLM training
• Move beyond manual QA layers by leveraging Python scripting, APIs, and automation frameworks to measure, validate, and improve dataset integrity
• Design and oversee tools or scripts for data validation, annotation accuracy checks, and pipeline consistency
• Ensure datasets adhere to compliance standards (PII, GDPR, HIPAA) and can be programmatically tested for usability and quality

LLM Training & Evaluation

• Lead generation and delivery of high-quality, scalable datasets focused on SFT, RLHF, reasoning, and agentic workflows
• Oversee the entire data lifecycle from client intake and annotation workflow design to delivery
• Partner with product, research, and engineering teams to implement evaluation metrics (e.g., win rate, inter-annotator agreement, and pairwise preference scoring)

Client Partnership & Communication

• Serve as the primary point of contact for enterprise AI clients; manage expectations, delivery timelines, and escalations
• Build relationships with engineering and research stakeholders by delivering consistently high-quality data
• Communicate effectively across technical and non-technical audiences; provide transparency through structured updates and quality reporting

Team Development & Tooling

• Recruit, mentor, and coach cross-functional leaders (Eng, Data, Ops, and Program Management)
• Drive adoption and improvement of internal tools (e.g., task management systems, quality dashboards)
• Champion continuous improvement across data quality, tools, and delivery processes

Required Qualifications

• 10+ years of experience leading large-scale technical delivery organizations, ideally across AI, ML, or data operations
• Bachelor's degree in Engineering, Computer Science, or equivalent technical discipline
• Demonstrated ability to act as a strategic business partner with our clients, researchers, and engineers at leading LLM labs
• Proven success in building and scaling multi-level high performance teams, with distributed global operations
• Experience managing managers
• Hands‑on technical fluency: ability to write and review data validation scripts
• Demonstrated experience managing dataset generation or annotation for machine learning model evaluation…