Summary: Partners with leading AI teams to improve the quality, usefulness, and reliability of general-purpose conversational AI systems by evaluating and refining engineering concepts. Highlights: 1. Opportunity to impact AI quality and reliability in engineering contexts 2. Engage with cutting-edge conversational AI systems 3. Apply deep engineering expertise to complex AI problems **Work Mode:** Remote **Engagement Type:** Independent Contractor **Schedule:** Full\-Time or Part\-Time Contract **Language Requirement:** Fluent English **Role :** 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
David Muñoz
Indeed · HR
David Muñoz
Indeed · HR
