Project Overview

Join a growing community of professionals advancing the next wave of AI. As an AI Trainer, you’ll play a hands-on role by analyzing and providing feedback on data to improve LLM performance, helping ensure that the next generation of AI technology is accurate and trustworthy.

We are seeking a skilled Manufacturing & Engineering Specialist to work as a project consultant in our AI Labor Marketplace. This is not a full-time employment position — you will be engaged as an expert project consultant on a contract basis.

Location: U.S.-based experts only

Engagement: Part-time, project-based expert evaluation work

Work Type: Remote

Project Summary

This project supports the development of an AI benchmarking framework designed to measure how effectively artificial intelligence models perform real-world, economically valuable knowledge work. The benchmark evaluates AI systems against tasks commonly performed by manufacturing and engineering professionals responsible for product development, process design, production operations, quality systems, reliability, technical problem-solving, operational efficiency, and engineering decision-making.

Contributors will create expert benchmark outputs, define evaluation criteria, and assess AI-generated work products across a wide range of engineering and manufacturing scenarios. Example tasks may include root cause analysis, process optimization, production planning, quality investigations, engineering change assessments, manufacturing risk analysis, technical documentation review, design evaluation, reliability analysis, capacity planning, supplier quality assessments, continuous improvement initiatives, and operational decision-making. The goal is to establish rigorous, expert-driven standards for measuring AI performance on practical engineering and manufacturing workflows.

Consultant Engagement Terms

This is a project-based consultant role. Consultants will be paid on a per-project basis; hourly rates are estimates based on anticipated completion time. Consultants control their own schedule, provide their own tools, and may simultaneously provide services to other vendors/employers (subject to those vendors’ allowances).

Responsibilities

Contributors will:

• Create expert benchmark responses for manufacturing and engineering-related professional scenarios
• Develop scoring rubrics that define high-quality technical reasoning, engineering analysis, operational decision-making, and problem-solving standards
• Evaluate AI-generated analyses, recommendations, reports, technical documentation, and operational deliverables against established benchmarks
• Assess the accuracy, completeness, technical validity, practicality, and business impact of AI-generated outputs
• Review AI-generated work involving manufacturing processes, engineering design, production systems, quality management, reliability engineering, maintenance strategies, and operational performance improvement
• Identify technical inaccuracies, engineering flaws, unsupported assumptions, process risks, quality concerns, and material omissions
• Apply professional judgment to determine whether outputs meet real-world engineering, manufacturing, and operational standards
• Evaluate AI-generated recommendations for feasibility, safety, quality implications, and implementation considerations
• Provide written rationale supporting evaluation decisions and scoring outcomes
• Participate in calibration exercises and quality review activities when required

Expected Outcomes

• High-quality benchmark responses reflecting engineering and manufacturing best practices
• Clear, defensible evaluation rubrics aligned with real-world technical and operational requirements
• Consistent and reliable assessment of AI-generated engineering and manufacturing work products
• Identification of strengths, weaknesses, technical risks, quality concerns, and performance gaps in model outputs
• Expert evaluations that support rigorous benchmarking of AI systems on economically valuable engineering and manufacturing work
• Documentation that enables repeatable and scalable model evaluation across technical domains

Qualifications

• Minimum 5 years of professional experience in manufacturing, engineering, industrial operations, product development, quality systems, process engineering, or related technical disciplines
• Strong understanding of engineering principles, manufacturing workflows, process improvement methodologies, quality management systems, and operational performance metrics
• Experience creating, reviewing, or evaluating technical analyses, engineering documentation, process designs, manufacturing procedures, quality investigations, or operational improvement initiatives
• Ability to analyze complex technical scenarios and articulate engineering reasoning clearly in writing
• Experience identifying process inefficiencies, technical risks, quality issues, root causes, and corrective actions
• Familiarity with structured problem-solving methodologies such as Lean Manufacturing, Six Sigma, DMAIC, PDCA, FMEA, Root Cause Analysis, Statistical Process Control (SPC), or similar frameworks
• Strong attention to detail and ability to identify inaccuracies, design flaws, process risks, and implementation challenges
• Experience working in manufacturing, industrial, aerospace, automotive, consumer products, medical devices, energy, electronics, chemicals, or related industries preferred
• Professional engineering licensure, technical certifications, or industry-recognized credentials are preferred where applicable (e.g., PE, Six Sigma Green Belt/Black Belt, ASQ Certified Quality Engineer, Certified Manufacturing Engineer, APICS/ASCM certifications, PMP, or equivalent)
• U.S.-based

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