Remote Chemical Materials&Process AI Evaluation Engineer

<p><strong>Work Mode:</strong>Remote<br><br><strong>Engagement Type:</strong>Independent Contractor<br><br><strong>Schedule:</strong>Full-Time or Part-Time Contract<br><br><strong>Language Requirement:</strong>Fluent English<br><br><strong>Role<br><br></strong>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.<br><br>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.<br><br><strong>What You’ll Do<br><br></strong><ul><li>Write and refine prompts to guide model behavior in engineering scenarios</li><li>Evaluate LLM-generated responses to engineering-related queries for technical accuracy, applied reasoning, and completeness</li><li>Conduct fact-checking and verify any technical claims using authoritative public sources and domain knowledge</li><li>Annotate model responses by identifying strengths, areas of improvement, and factual or conceptual inaccuracies</li><li>Assess clarity, structure, and appropriateness of explanations for different audiences</li><li>Ensure model responses align with expected conversational behavior and system guidelines</li><li>Apply consistent evaluation standards by following clear taxonomies, benchmarks, and detailed evaluation guidelines<br><br></li></ul><strong>Who You Are</strong><br><br><ul><li>You hold a PhD in Engineering or a closely related field</li><li>You have deep expertise in one or more of the following sub-domains:</li><ul><li>Mechanical&Physical Systems Engineering</li><li>Electrical, Electronic&Computer Engineering</li><li>Chemical, Materials&Process Engineering</li><li>Civil, Environmental&Infrastructure Engineering</li></ul><li>You have significant experience using large language models (LLMs) and understand how and why people use them</li><li>You have excellent writing skills and can clearly explain complex engineering concepts</li><li>You have strong attention to detail and consistently notice subtle issues others may overlook</li><li>Experience reviewing or editing technical or academic writing<br></li></ul><strong>Nice-to-Have Specialties<br><br></strong><ul><li>Experience with applied research, industry engineering workflows, or systems design</li><li>Prior experience with RLHF, model evaluation, or data annotation work</li><li>Experience teaching, mentoring, or explaining engineering concepts to non-expert audiences</li><li>Familiarity with evaluation rubrics, benchmarks, or structured review frameworks<br><br></li></ul><strong>What Success Looks Like<br><br></strong><ul><li>You identify technical inaccuracies, flawed assumptions, or incomplete reasoning in engineering-related model outputs</li><li>Your feedback improves the rigor, clarity, and correctness of AI explanations</li><li>You deliver consistent, reproducible evaluation artifacts that strengthen model performance</li></ul></p>#J-18808-Ljbffr