ABOUT TURING

Based in Palo Alto, California, 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.

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 track record in benchmark development, model evaluation, or data-centric infrastructure.
• Strategic thinker with a bias toward impact: can connect data quality work directly to client value.
• Experience designing and interpreting metrics that inform delivery performance.
• Familiarity with annotation workflows, validation processes, and scalable QA systems.
• Solid ML or data science foundation—able to reason about training impact from a data point-of-view.

ADVANTAGES OF JOINING TURING:

• Amazing work culture (Super collaborative & supportive work environment; 5 days a week)
• Awesome colleagues (Surround yourself with top talent from Meta, Google, LinkedIn etc. as well as people with deep startup experience)
• Competitive compensation
• Flexible working hours
• Full-time remote opportunity
  Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.
  For applicants from the European Union, please review Turing’s GDPR notice here.

ROLE OVERVIEW

We are looking for a strategic, data-savvy individual to lead efforts in evaluating data quality and its return on investment—particularly as it impacts downstream model outcomes in terms of improving their performance on challenging benchmarks and workflows in the real-world. This is a high-leverage, cross-functional role focused on delivery excellence and maximizing client impact through smarter data practices.

Rather than training models directly, this role is about answering 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?

KEY 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.