LLM Data Researcher

Position Overview

• Location Type: Hybrid
• Employment Type: Full-time
• Salary: Not specified

Turing is one of the world's fastest-growing AI companies accelerating the advancement and deployment of powerful AI systems. Turing helps customers in two ways: working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilingualism, STEM and frontier knowledge; and leveraging that expertise to build real-world AI systems that solve mission-critical priorities for Fortune 500 companies and government institutions. Turing has received numerous awards, including Forbes's "One of America's Best Startup Employers," #1 on The Information's annual list of "Most Promising B2B Companies," and Fast Company's annual list of the "World's Most Innovative Companies." Turing's leadership team includes AI technologists from industry giants Meta, Google, Microsoft, Apple, Amazon, Twitter, McKinsey, Bain, Stanford, Caltech, and MIT. For more information on Turing, visit www.turing.com. For information on upcoming Turing AGI Icons events, visit go.turing.com/agi-icons.

This role is focused on evaluating data quality and its return on investment, particularly as it impacts downstream model outcomes. It's a high-leverage, cross-functional position centered on delivery excellence and maximizing client impact through smarter data practices. The role focuses on foundational questions such as:

• What types of data move the needle most on model performance?
• How do we build benchmarks that matter and reflect real-world use cases?
• Where do we invest next to scale high-quality data pipelines?

Responsibilities

• Define and implement strategies to assess the ROI of data across training and fine-tuning pipelines.
• Build and maintain benchmarks that measure performance across key client and internal objectives.
• Collaborate with data operations, research, and delivery teams to align on quality standards and data priorities.
• Develop systems and tooling for continuous data evaluation—measuring what matters, where it matters.
• Drive human-in-the-loop quality processes including pre-delivery validation and annotation feedback loops.
• Identify data gaps and lead targeted acquisitions or refinements, guided by performance metrics.
• Define and/or leverage comprehensive task taxonomy frameworks to structure data annotation efforts and improve training signal quality.
• Translate research insights and data evaluations into client-facing value through better delivery and prioritization.

Required Qualifications

• Strong grasp of LLMs and data-model dynamics (but this is not a model training role).
• Knowledge of the latest trends in Generative AI and data that is useful for improving foundation models.
• Proven ability to assess and interpret data quality metrics.