Engineering Expert – AI Systems Evaluation
Job Description:
• 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
Requirements:
• 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
• Applied research, industry engineering workflows, or systems design (preferred)
• Experience with reinforcement learning from human feedback (RLHF), model evaluation, or structured data annotation (preferred)
• Teaching, mentoring, or explaining engineering concepts to non-expert audiences (preferred)
• Familiarity with structured evaluation rubrics, benchmarks, or quality assurance frameworks (preferred)
Benefits:
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• 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
Requirements:
• 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
• Applied research, industry engineering workflows, or systems design (preferred)
• Experience with reinforcement learning from human feedback (RLHF), model evaluation, or structured data annotation (preferred)
• Teaching, mentoring, or explaining engineering concepts to non-expert audiences (preferred)
• Familiarity with structured evaluation rubrics, benchmarks, or quality assurance frameworks (preferred)
Benefits:
Apply Now
Apply Now