Evaluate AI responses for accuracy and improve model performance. 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 deploying advanced AI systems.
About the role
Evaluate AI responses for accuracy and improve model performance. 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 deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in coding, reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L.
Role Overview
We are seeking highly motivated Team Management to support the evaluation and improvement of cutting-edge Large Language Models (LLMs). In this role, you will analyze structured data, create challenging prompts, evaluate AI-generated responses for factual accuracy and reasoning quality, and provide evidence-backed feedback to improve model performance.
We are looking for curious, detail-oriented professionals who enjoy solving complex problems and evaluating AI systems. The ideal candidate can analyze data, think critically, validate responses against evidence, and clearly articulate why a model's output is correct or incorrect. Experience working with AI models and creating challenging evaluation prompts is a strong advantage.
Key Responsibilities
Create challenging prompts that evaluate an LLM's ability to retrieve, analyze, and reason over structured data.
Assess AI-generated responses for factual accuracy, logical reasoning, and completeness.
Identify model failures, inconsistencies, hallucinations, and reasoning gaps.
Validate model outputs using provided datasets and supporting evidence.
Document findings with clear, evidence-based explanations. …
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