Delivery Manager Data Science US

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

Delivery Manager – Data Science (AI / LLM / Agentic Systems)

Location: San Francisco, California, United States onsite

                  India/ Brazil - Remote 

GH Link US

GH Link India 

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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

Turing is seeking an accomplished Delivery Manager – Data Science to lead large-scale LLM training and Agentic AI programs, driving the successful delivery of mission-critical datasets, fine-tuning workflows, and model alignment initiatives.

In this role, you will oversee multiple cross-functional teams of data scientists, ML engineers, and data professionals engaged in Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and Agentic AI system development. You’ll ensure the delivery of world-class training data, reproducible pipelines, and scalable experimentation frameworks that directly support frontier AI research and enterprise-grade AI deployments.

This is a high-impact leadership role at the intersection of technical depth, delivery excellence, and strategic alignment — ideal for someone who combines expertise in LLMs with operational mastery of large-scale AI execution.

Primary Responsibilities

1. Team and Delivery Leadership

• Lead large, distributed teams of data scientists, ML engineers, and Python developers delivering high-quality AI datasets and models.
  
  
• Scale and operationalize data science workflows for SFT, RLHF, and RLAIF pipelines.
  
  
• Define and manage project goals, timelines, and quality benchmarks for multiple concurrent programs.
  
  
• Drive team performance, engagement, and innovation, fostering a culture of technical excellence and accountability.
  
  

2. Program and Stakeholder Management

• Oversee end-to-end delivery of complex AI/ML initiatives, ensuring client goals are achieved on schedule and within scope.
  
  
• Partner with internal research and product leaders to translate technical requirements into actionable execution plans.
  
  
• Manage dependencies across annotation, model training, and evaluation streams, mitigating risks proactively.
  
  
• Deliver clear and consistent program reporting, highlighting key metrics, challenges, and insights.
  
  

3. Technical Oversight and Data Quality

• Ensure rigorous standards for data, model, and process quality throughout the lifecycle.
  
  
• Oversee data collection and benchmarking for LLM alignment and evaluation.
  
  
• Leverage analytics to identify model performance gaps, biases, and optimization opp