Chemistry (PhD)

Fluent Language Skills Required: English

Why This Role Exists

Fluent Language Skills Required

Why This Role Exists

What You’ll Do

Write and refine prompts

Evaluate LLM-generated responses

Conduct fact-checking

Annotate model responses

Assess clarity, structure, and appropriateness of explanations

model responses align with expected conversational behavior

Apply consistent evaluation standards

Who You Are

PhD in Chemistry or a closely related field

one or more of the following sub-domains

significant experience using large language models

excellent writing skills

strong attention to detail

Experience reviewing or editing technical or academic writing

Nice-to-Have Specialties

What Success Looks Like

Why Join Mercor

Fluent Language Skills Required: English Why This Role Exists Mercor partners with leading AI teams to improve the quality, usefulness, and reliability of general-purpose conversational AI systems. These systems are used across a wide range of everyday and professional scenarios, and their effectiveness depends on how clearly, accurately, and helpfully they respond to real user questions. In chemistry-related contexts, conversational AI systems must demonstrate accurate mechanistic reasoning, quantitative precision, and a strong understanding of chemical principles across theoretical and applied settings. This project focuses on evaluating and improving how models reason about, explain, and communicate chemical concepts across foundational and advanced topics. What You’ll Do Write and refine prompts to guide model behavior in chemistry contexts Evaluate LLM-generated responses to chemistry-related queries for scientific accuracy, mechanistic correctness, and quantitative reasoning Conduct fact-checking using authoritative public sources and domain knowledge Annotate model responses by identifying strengths, areas of improvement, and factual or conceptual inaccuracies Assess clarity, structure, and appropriateness of explanations for different audiences Ensure model responses align with expected conversational behavior and system guidelines Apply consistent evaluation standards by following clear taxonomies, benchmarks, and detailed evaluation guidelines Who You Are You hold a PhD in Chemistry or a closely related field You have deep expertise in one or more of the following sub-domains: Organic & Biological Chemistry Inorganic & Materials Chemistry Physical & Theoretical Chemistry Analytical & Instrumental Chemistry You have significant experience using large language models (LLMs) and understand how and why people use them You have excellent writing skills and can clearly explain complex chemistry concepts You have strong attention to detail and consistently notice subtle issues others may overlook Experience reviewing or editing technical or academic writing Nice-to-Have Specialties Prior experience with RLHF, model evaluation, or data annotation work Experience teaching, mentoring, or explaining chemistry concepts to non-expert audiences Familiarity with evaluation rubrics, benchmarks, or structured review frameworks What Success Looks Like You identify inaccuracies or weak reasoning in chemistry-related model outputs Your feedback improves the rigor, clarity, and correctness of AI explanations You deliver consistent, reproducible evaluation artifacts that strengthen model performance Mercor customers trust their AI systems in chemistry contexts because you’ve rigorously evaluated them Why Join Mercor At Mercor, PhD-level chemists have the opportunity to apply their expertise beyond traditional academic or industry roles, directly shaping how advanced AI systems reason about and explain chemical concepts. This role allows you to contribute to high-impact AI development work while maintaining autonomy over your schedule. We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.

• Write and refine prompts to guide model behavior in chemistry contexts
• Evaluate LLM-generated responses to chemistry-related queries for scientific accuracy, mechanistic correctness, and quantitative reasoning
• Conduct fact-checking using authoritative public sources and domain knowledge
• Annotate model responses by identifying strengths, areas of improvement, and factual or conceptual inaccuracies
• Assess clarity, structure, and appropriateness of explanations for different audiences
• Ensure model responses align with expected conversational behavior and system guidelines
• Apply consistent evaluation standards by following clear taxonomies, benchmarks, and detailed evaluation guidelines
• You hold a PhD in Chemistry or a closely related field
• You have deep expertise in one or more of the following sub-domains:

Organic & Biological Chemistry

Inorganic & Materials Chemistry

Physical & Theoretical Chemistry

Analytical & Instrumental Chemistry

• Organic & Biological Chemistry
• Inorganic & Materials Chemistry
• Physical & Theoretical Chemistry
• Analytical & Instrumental Chemistry
• You have significant experience using large language models (LLMs) and understand how and why people use them
• You have excellent writing skills and can clearly explain complex chemistry concepts
• You have strong attention to detail and consistently notice subtle issues others may overlook
• Experience reviewing or editing technical or academic writing
• Organic & Biological Chemistry
• Inorganic & Materials Chemistry
• Physical & Theoretical Chemistry
• Analytical & Instrumental Chemistry
• Prior experience with RLHF, model evaluation, or data annotation work
• Experience teaching, mentoring, or explaining chemistry concepts to non-expert audiences
• Familiarity with evaluation rubrics, benchmarks, or structured review frameworks
• You identify inaccuracies or weak reasoning in chemistry-related model outputs
• Your feedback improves the rigor, clarity, and correctness of AI explanations
• You deliver consistent, reproducible evaluation artifacts that strengthen model performance
• Mercor customers trust their AI systems in chemistry contexts because you’ve rigorously evaluated them