Engineering Experts (Remote)

Nexus Consulting

United States

Remote

USD 90,000 - 150,000

Full time

14 days+

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Benefits offered by this job

Flexible working hours
Weekly payments based on completed work
Flexible hours

Job summary

A technology consulting firm is seeking PhD-level engineering experts to support projects aimed at improving advanced conversational AI systems' accuracy and reliability. The role involves evaluating AI outputs, fact-checking engineering claims, and ensuring technical accuracy. Candidates should possess a PhD in Engineering, expertise in various engineering fields, and experience with large language models. This position offers flexible remote work with weekly payments based on completed tasks.

Qualifications

  • PhD in Engineering or a closely related field required.
  • Experience using large language models (LLMs).
  • Strong technical writing and communication skills necessary.

Responsibilities

  • Write and refine prompts for engineering-related AI tasks.
  • Evaluate AI-generated responses for technical accuracy.
  • Fact-check engineering claims using reliable public sources.
  • Annotate responses by identifying strengths, weaknesses, and inaccuracies.
  • Assess clarity, structure, and explanation quality for different audiences.
  • Ensure responses follow evaluation guidelines and quality standards.
  • Apply structured evaluation frameworks and benchmarks consistently.

Skills

Expertise in Mechanical & Physical Systems Engineering
Expertise in Electrical, Electronic & Computer Engineering
Expertise in Chemical, Materials & Process Engineering
Expertise in Civil, Environmental & Infrastructure Engineering
Strong technical writing and communication skills
Excellent attention to detail

Education

PhD in Engineering or a closely related field

Tools

LLMs
Evaluation frameworks

Job description

We are seeking PhD-level Engineering experts to support projects focused on improving the accuracy, usefulness, and reliability of advanced conversational AI systems.

In this role, you will help evaluate how AI models reason about engineering concepts and ensure their explanations demonstrate accurate applied reasoning, quantitative precision, and practical problem-solving aligned with real-world engineering systems.

Key Responsibilities
  • Write and refine prompts for engineering-related AI tasks
  • Evaluate AI-generated responses for technical accuracy and applied reasoning
  • Fact-check engineering claims using reliable public sources and domain expertise
  • Annotate responses by identifying strengths, weaknesses, and inaccuracies
  • Assess clarity, structure, and explanation quality for different audiences
  • Ensure responses follow evaluation guidelines and quality standards
  • Apply structured evaluation frameworks and benchmarks consistently
Requirements
  • PhD in Engineering or a closely related field
  • Expertise in one or more of the following areas:
  • Mechanical & Physical Systems Engineering
  • Electrical, Electronic & Computer Engineering
  • Chemical, Materials & Process Engineering
  • Civil, Environmental & Infrastructure Engineering
  • Experience using large language models (LLMs)
  • Strong technical writing and communication skills
  • Excellent attention to detail
Nice to Have
  • Experience with applied research or engineering systems design
  • Experience with AI model evaluation, RLHF, or data annotation
  • Teaching or mentoring experience in engineering topics
  • Familiarity with evaluation frameworks or benchmarking methods
  • Experience reviewing or editing technical or academic writing
What Success Looks Like
  • Identify technical inaccuracies or weak reasoning in engineering-related AI outputs
  • Provide feedback that improves clarity, rigor, and correctness of explanations
  • Deliver consistent evaluation results aligned with project guidelines
  • Contribute to improving AI systems used in engineering and technical applications
  • Fully remote with flexible working hours
  • Weekly payments based on completed work
  • Project scope may change depending on performance and project requirements
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