Team Management

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. About the Role We are seeking highly qualified Team Management professionals to support the evaluation, testing, and continuous improvement of advanced Large Language Models (LLMs). In this role, you will apply your expertise in team leadership, people management, performance management, workforce planning, and operational execution to assess AI-generated responses. You will identify knowledge gaps, design challenging real-world prompts, evaluate model reasoning, and help improve the accuracy, reliability, and practical usefulness of AI systems. Key Responsibilities Create complex, domain-specific prompts to test LLM capabilities. Evaluate AI-generated responses for factual accuracy, reasoning, relevance, and overall quality. Identify factual errors, logical inconsistencies, knowledge gaps, and model weaknesses. Develop challenging test cases and edge cases that expose model limitations. Provide clear, detailed, and evidence-based feedback to improve model performance. Minimum Qualifications Master’s degree or higher in a relevant field. At least three years of professional, research, or teaching experience. Excellent written English communication skills. Strong analytical and critical-thinking abilities. Exceptional attention to detail. Preferred Qualifications Experience working with LLMs or Generative AI. Familiarity with prompt engineering, AI evaluation, or model testing. Experience designing challenging prompts, identifying edge cases, or stress-testing AI models. Ideal Candidate The ideal candidate is curious, detail-oriented, and deeply knowledgeable in their field. They enjoy solving complex problems, challenging assumptions, and identifying subtle factual or reasoning errors. Experience designing prompts that uncover model limitations and improve AI performance will be a strong advantage.