Engineering (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 and verify any technical claims

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 Engineering 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 engineering-related contexts, conversational AI systems must demonstrate accurate applied reasoning, quantitative precision, and practical problem-solving aligned with real-world systems. This project focuses on evaluating and improving how models reason about and explain engineering concepts across multiple disciplines. What You’ll Do Write and refine prompts to guide model behavior in engineering scenarios Evaluate LLM-generated responses to engineering-related queries for technical accuracy, applied reasoning, and completeness Conduct fact-checking and verify any technical claims 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 Engineering or a closely related field You have deep expertise in one or more of the following sub-domains: Mechanical & Physical Systems Engineering Electrical, Electronic & Computer Engineering Chemical, Materials & Process Engineering Civil, Environmental & Infrastructure Engineering 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 engineering 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 Experience with applied research, industry engineering workflows, or systems design Prior experience with RLHF, model evaluation, or data annotation work Experience teaching, mentoring, or explaining engineering concepts to non-expert audiences Familiarity with evaluation rubrics, benchmarks, or structured review frameworks What Success Looks Like You identify technical inaccuracies, flawed assumptions, or incomplete reasoning in engineering-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 engineering contexts because you’ve rigorously evaluated them Why Join Mercor At Mercor, PhD-level engineers can apply their expertise to improve how AI systems reason about and communicate complex technical concepts. This flexible, remote role enables you to contribute directly to the development of reliable, high-quality AI systems used in real-world applications. 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 engineering scenarios
• Evaluate LLM-generated responses to engineering-related queries for technical accuracy, applied reasoning, and completeness
• Conduct fact-checking and verify any technical claims 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 Engineering or a closely related field
• You have deep expertise in one or more of the following sub-domains:

Mechanical & Physical Systems Engineering

Electrical, Electronic & Computer Engineering

Chemical, Materials & Process Engineering

Civil, Environmental & Infrastructure Engineering

• Mechanical & Physical Systems Engineering
• Electrical, Electronic & Computer Engineering
• Chemical, Materials & Process Engineering
• Civil, Environmental & Infrastructure Engineering
• 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 engineering concepts
• You have strong attention to detail and consistently notice subtle issues others may overlook
• Experience reviewing or editing technical or academic writing
• Mechanical & Physical Systems Engineering
• Electrical, Electronic & Computer Engineering
• Chemical, Materials & Process Engineering
• Civil, Environmental & Infrastructure Engineering
• Experience with applied research, industry engineering workflows, or systems design
• Prior experience with RLHF, model evaluation, or data annotation work
• Experience teaching, mentoring, or explaining engineering concepts to non-expert audiences
• Familiarity with evaluation rubrics, benchmarks, or structured review frameworks
• You identify technical inaccuracies, flawed assumptions, or incomplete reasoning in engineering-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 engineering contexts because you’ve rigorously evaluated them