Review and improve real-world legal scenarios to train AI assistants by identifying flawed reasoning and procedural gaps. Upgrade weak scenarios to ensure they effectively test legal judgment across various international jurisdictions.
Review and improve real-world technical scenarios used to train AI assistants. Identify logical flaws, edge cases, and bad practices to ensure high-quality technical judgment in AI training data.
Review and improve educational scenarios used to train AI teaching assistants. Ensure learning interactions are effective and test the model's capabilities.
Contributors will role-play specific political perspectives in multi-turn conversations with AI systems to test neutrality and balance. They are responsible for justifying their evaluations based on evidence from the interactions.
Review and improve real-world technical engineering scenarios to train AI planning assistants for educational purposes. Act as a senior engineer and educator to map decision trees, identify hypotheses, and ensure technical reasoning is sound.
You will build structured materials to identify and justify comparable companies for target businesses to train AI systems. This involves applying financial expertise to articulate the logic behind peer group selection based on business models and market dynamics.
Define and justify essential financial metrics and KPIs based on company filings and business models. Interpret multi-year financial results to identify trends, accounting effects, and sector-specific drivers.
Review and improve real-world business scenarios used to train AI planning assistants. Structure complex problems and provide clear recommendations based on strategic reasoning.
Review and improve AI-generated health scenarios to ensure they accurately reflect real-world clinical interactions. Evaluate scenarios for realism, safety risks, and the ability to test AI models effectively.
Review and improve realistic financial scenarios to train AI financial assistants by identifying and addressing critical decision constraints. You will rate task quality and rewrite scenarios to ensure they effectively test model performance under complex financial conditions.
Review and audit tasks produced by human annotators to ensure high data quality standards. Identify AI-generated content and ensure compliance with established guidelines and evaluation criteria.
Perform role-play sessions as a clinically informed persona to interact with AI systems in sensitive scenarios. Evaluate AI responses for safety, empathy, and clinical accuracy while providing structured feedback.
Nutrition experts will review and improve real-world scenarios used to train AI planning assistants in clinical and educational contexts. You will be responsible for breaking down dietary challenges, identifying alternatives, and justifying decisions with concrete data.
Nutrition experts will review and improve real-world scenarios to train AI planning assistants in clinical and educational contexts. Responsibilities include breaking down dietary challenges, mapping decision trees, and justifying clinical decisions with concrete data.
Design and execute post-training experiments for LLMs, including SFT and preference-based methods. Translate raw expert data into training-ready datasets and iterate on reward signals and evaluation pipelines.
Engage in structured multi-turn role-play conversations with an AI system while maintaining a specific persona and scenario. Ensure emotional authenticity and realism throughout the conversation to facilitate clinical evaluation of the AI's responses.
Marketing experts will review and improve real-world scenarios used to train AI planning assistants. They are responsible for breaking down marketing challenges, mapping decision trees, and justifying decisions with data to ensure rigorous educational content.
The role involves reviewing real case scenarios to assess sales diagnosing skills and providing feedback. You will act as a Shopping Expert to evaluate sales processes and thought patterns.
Review and validate generated code against software engineering prompts to ensure technical accuracy and problem-solving effectiveness. Evaluate test suites for robustness and identify gaps or ambiguities in code quality.
Review and improve travel scenarios to train AI travel assistants by identifying feasibility constraints and logical gaps. You will rate and rewrite tasks to ensure they accurately test the model's ability to handle real-world travel challenges.
Review and improve real-world wellness scenarios to train AI wellness assistants. Identify risks, contraindications, and behavioral patterns to ensure scenarios test professional judgment effectively.
