Language: English

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. Main tasks listed: 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.

We are looking for a detail-oriented QA Analyst to evaluate and compare responses generated by multiple chatbots. The role involves reviewing outputs against defined guidelines, identifying inaccuracies or quality issues, and providing a clear final assessment for each evaluation task. Main tasks listed: Evaluate and compare responses generated by multiple chatbots; Assess responses for accuracy, relevance, completeness, clarity, and adherence to guidelines; Identify errors, inconsistencies, missing information, and potential quality issues; Provide structured feedback and final assessments for each task.

We are seeking experienced in Biology help evaluate and enhance large language model (LLM) outputs related to nutrition, dietary habits, and healthy eating. You’ll assess AI-generated content for scientific accuracy, safety, and relevance — contributing to the development of responsible AI in nutrition and wellness. Selected talents will label and evaluate data related to food intake, dietary patterns, and nutrition information to support AI models focused on nutrition tracking, coaching, or research. Main tasks listed: Annotate food items, portion sizes, meal composition, and nutrient content from text, images, or logs; Classify dietary patterns (e.g., macronutrient balance, meal timing, dietary restrictions/preferences); Verify accuracy of nutritional data against trusted references (e.g., food composition databases); Identify and flag mislabeled, inconsistent, or culturally ambiguous food entries.

We are looking for senior software engineers with 7+ years of industry experience to support the training and evaluation of large language models. In this role, you will review, analyze, and improve AI-generated code, ensuring that it is correct, secure, maintainable, scalable, and aligned with production engineering standards. This is a code quality and engineering evaluation role—not a traditional software testing or manual QA position.

We are seeking Life Sciences Subject Matter Experts to design advanced scientific-reasoning tasks for LifeSciBench. This is a non-coding role focused on biological knowledge, experimental interpretation, data analysis, and rigorous scientific reasoning. You will create challenging, objectively defensible problems based on realistic research scenarios, experimental results, biological data, figures, sequences, structures, and literature-style evidence. Main tasks listed: Design novel, graduate-level life sciences problems grounded in realistic research contexts; Develop clear questions and rigorous reference answers with defensible scientific reasoning; Interpret experiments, datasets, figures, sequences, structures, and other biological evidence; Validate biological claims, calculations, assumptions, and conclusions.

We are looking for a Strategic Project Lead with direct experience operating software-engineering data programs for frontier AI models. You will own production across coding data, RLHF or preference data, SWE benchmarks, model evaluations, agentic coding, code generation, and code repair. This includes contributor calibration, throughput, quality, rework, cost, timelines, and delivery recovery. The role requires strong technical judgment.

Turing builds large-scale datasets and reinforcement learning environments that power post-training for leading AI labs and enterprises. Our environments evaluate and improve AI models on complex, long-range, multi-step workflows across high-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, and Customer Experience. Main tasks listed: Conduct Research on Frontier AI Systems; Investigate the capabilities, limitations, and training methods of frontier AI systems; Formulate research questions that inform Turing’s products, platforms, and technical strategy; Explore new approaches to synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation.

The Text Situated Alignment Generalist – BA role supports a short-term human-AI interaction evaluation pilot designed to assess whether providing social context before a user’s request helps a language model generate more relevant, personalized, and user-aligned advice. Contributors create realistic, ongoing social scenarios, interact with the model under different conditions, and evaluate the quality of its responses. Main tasks listed: Create realistic and ongoing social scenarios involving personal, professional, or interpersonal context; Interact with the language model under assigned evaluation conditions; Evaluate model responses for relevance, usefulness, personalization, clarity, and alignment with the provided context; Explain the reasoning behind each rating through clear and detailed written feedback.

The Text Situated Alignment Generalist – BA role supports a short-term human-AI interaction evaluation pilot designed to assess whether providing social context before a user’s request helps a language model generate more relevant, personalized, and user-aligned advice. Contributors create realistic, ongoing social scenarios, interact with the model under different conditions, and evaluate the quality of its responses. Main tasks listed: Create realistic and ongoing social scenarios involving personal, professional, or interpersonal context; Interact with the language model under assigned evaluation conditions; Evaluate model responses for relevance, usefulness, personalization, clarity, and alignment with the provided context; Explain the reasoning behind each rating through clear and detailed written feedback.

We are seeking Life Sciences Subject Matter Experts to design advanced scientific-reasoning tasks for LifeSciBench. This is a non-coding role focused on biological knowledge, experimental interpretation, data analysis, and rigorous scientific reasoning. You will create challenging, objectively defensible problems based on realistic research scenarios, experimental results, biological data, figures, sequences, structures, and literature-style evidence. Main tasks listed: Design novel, graduate-level life sciences problems grounded in realistic research contexts; Develop clear questions and rigorous reference answers with defensible scientific reasoning; Interpret experiments, datasets, figures, sequences, structures, and other biological evidence; Validate biological claims, calculations, assumptions, and conclusions.