Energy Project Estimator - Remote Part-Time

What You'll Do7

• Review AI-generated answers about energy project estimation, construction planning, and solar design for accuracy and relevance.
• Use hands-on knowledge of Bluebeam Revu, RSMeans, or Aurora Solar to spot errors in model reasoning about cost estimation and project workflows.
• Write expert training data that explains how you approach estimates, pre-construction decisions, or solar design tasks.
• Provide clear written feedback that shows the correct method or missing detail in an AI response.
• Flag outputs that misstate costs, overlook construction steps, or misapply solar design principles.
• Follow project guidelines and communicate with AI research teams through async channels.
• Maintain consistent quality across batches of evaluation tasks.

Requirements7

• At least 2 years of professional experience in energy project estimation, pre-construction, or solar design.
• Background as an Estimator, Project Engineer, Pre-construction Manager, Solar Design Engineer, or a comparable energy industry role.
• Hands-on use of at least one tool from this list: Bluebeam Revu, RSMeans, Aurora Solar.
• Strong writing skills with the ability to explain technical decisions in plain language.
• Careful attention to detail when reviewing numbers, scope, and project assumptions.
• Ability to work on your own and keep pace with an async, remote project schedule.
• No prior AI experience required.

Who Should Apply

The best fit is an energy professional with 2 or more years in estimation, pre-construction, or solar design who has used Bluebeam Revu, RSMeans, or Aurora Solar on real projects. You should enjoy writing short, clear explanations and feel comfortable with an async, remote workflow that you schedule around other commitments. This role suits someone who wants project-based work, not a permanent full-time position. Candidates often miss the mark when they claim energy experience without showing specific estimation, pre-construction, or solar design work, or when they list tools but cannot describe how they used them. Weak written feedback also lowers fit, since the project depends on detailed evaluation notes.

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