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Electrical Electronic Engineer (PhD) - Remote

YO IT Consulting, UAE1 months agoMid-Senior
Mid-Seniorfulltime

Skills

engineeringdesignproject management

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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