Electrical Electronic Engineer (PhD) - Remote
Skills
About This Role
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:
+ 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
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