Engineering Expert (PhD) - AI Systems Evaluation

This role is for one of our clients

Compensation: $73.29 per hour

PhD-level engineers are sought to support high-impact collaborations with advanced AI research teams. This role focuses on improving the accuracy, rigor, and reliability of general-purpose conversational AI systems, particularly in engineering-related contexts.

AI systems used in professional engineering scenarios must demonstrate strong applied reasoning, quantitative accuracy, and alignment with real-world systems. This project centers on evaluating and enhancing how models interpret, reason about, and explain engineering concepts across multiple disciplines.

Requirements:
Key Responsibilities

• Develop and refine prompts to guide AI behavior in engineering-specific scenarios
• Evaluate model-generated responses for technical correctness, applied reasoning, completeness, and practical relevance
• Fact-check technical claims using authoritative public sources and domain expertise
• Annotate outputs by identifying conceptual gaps, flawed assumptions, and factual inaccuracies
• Assess clarity, structure, and appropriateness of explanations for various audiences
• Ensure responses align with expected conversational standards and system-level guidelines
• Apply structured evaluation frameworks, taxonomies, and benchmarking standards consistently

Required Qualifications

• PhD in Engineering or a closely related field
• Deep expertise in one or more of the following domains:
• Mechanical & Physical Systems Engineering
• Electrical, Electronic & Computer Engineering
• Chemical, Materials & Process Engineering
• Civil, Environmental & Infrastructure Engineering

• Strong familiarity with large language models (LLMs) and their practical applications
• Excellent written communication skills with the ability to clearly explain complex technical concepts
• High attention to detail and ability to detect subtle technical inaccuracies
• Experience reviewing, editing, or critiquing technical or academic writing

Preferred Experience

• Applied research, industry engineering workflows, or systems design
• Experience with reinforcement learning from human feedback (RLHF), model evaluation, or structured data annotation
• Teaching, mentoring, or explaining engineering concepts to non-expert audiences
• Familiarity with structured evaluation rubrics, benchmarks, or quality assurance frameworks

What Success Looks Like

• You consistently identify technical inaccuracies, incomplete reasoning, or flawed assumptions in engineering-related AI outputs
• Your structured feedback measurably improves the rigor, clarity, and correctness of model responses
• You produce consistent, reproducible evaluation artifacts that strengthen model performance over time
• Engineering-focused AI systems demonstrate greater reliability and trustworthiness as a result of your evaluations

Contract & Payment Terms

• Engagement will be structured as an independent contractor agreement
• Fully remote with flexible scheduling
• Projects may be extended, shortened, or concluded early based on performance and evolving needs
• Assignments will not require access to confidential or proprietary information from any employer, client, or institution
• Payments are processed weekly via Stripe or Wise based on services rendered
• Visa sponsorship is not available; H1-B and STEM OPT candidates cannot be supported at this time