AI Software Engineering Domain Expert

What you would do

• Analyze AI-generated technical responses for correctness, completeness, clarity, and alignment with software engineering standards and best practices
• Author and refine prompts and instructions designed to guide AI in producing high-quality technical documentation and solutions
• Evaluate AI outputs using rubrics that assess technical accuracy, explanation quality, and professional communication
• Draft technical specifications, architecture plans, design proposals, and other documentation for use in AI training
• Conduct research and fact-checking to validate technical claims and ensure accuracy in training content

Who they want

• 3+ years professional software engineering, technical leadership, or solutions architecture experience
• Track record authoring or reviewing technical artifacts: documentation, architecture documents, design specs, RFCs, code reviews, technical proposals
• Strong capability for abstract thinking, technical analysis, and systematic problem-solving approaches
• Careful focus on precision and quality standards when evaluating and authoring technical materials
• Strong written communication skills with experience in professional technical writing and editing

Main skills

What the interview asks about

1. 1.Technical correctness judgment

   An API explanation might sound authoritative but contain subtle errors-wrong return types, missing error cases, incorrect assumptions. You must spot these.

   For example: “An AI explains a REST API endpoint incorrectly stating that a 404 means 'not found' when the spec shows 404 is used for rate limiting. How would you catch and correct this?”

2. 2.Prompt clarity and effectiveness

   Vague prompts produce verbose, unfocused AI outputs. Clear prompts produce concise, correct answers. Small wording changes matter significantly.

   For example: “You're trying to get AI to produce an architecture document for a microservice system. Your first prompt produced 50 pages of generic architecture theory. What would you refine in the next prompt?”

3. 3.Completeness assessment

   AI might produce correct but incomplete technical work-an RFC that explains decisions but omits the implementation strategy, or documentation missing edge cases.

   For example: “An AI-generated design spec for a caching layer is technically sound but omits how it handles cache invalidation under concurrent updates. Is this acceptable, or what's missing?”

4. 4.Best practices and standards

   Technically correct code might violate team standards: error handling patterns, logging conventions, naming schemes. You must evaluate against contextual standards.

   For example: “AI-generated code correctly implements a feature but uses a non-standard error handling pattern compared to the team's codebase conventions. Do you accept or request revision?”

5. 5.Research validation under ambiguity

   Technical claims require verification: performance numbers might be outdated, APIs might have changed, assumptions about system behavior might be wrong.

   For example: “An AI claims a specific database query optimization technique reduces latency by 40%. You need to validate this claim without access to the team's actual systems. How do you approach this?”

A task you may get

Review an AI technical document (design spec, architecture plan, or RFC). Identify errors, omissions, and clarity issues. Refine the prompt that produced it, then draft an evaluation rubric for scoring.

How to prepare

• Review one RFC or architecture document from a real engineering team, noting structure, depth, trade-offs discussed, and communication quality
• Write a technical prompt for AI, get a response, assess what worked and what didn't, then refine the prompt for a second iteration
• Fact-check 2-3 technical claims from AI-generated content against official documentation or research papers to practice validation rigor
• Draft a simple rubric for evaluating technical documentation quality, including criteria for accuracy, completeness, clarity, and actionability

The facts

Pay

$100–200/hr

Open to

Bangladesh, Hong Kong, India, Indonesia, Japan, Kazakhstan, Kyrgyzstan, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka, Taiwan, Thailand, Uzbekistan, Vietnam, Austria, Belarus, Belgium, Denmark, France, Germany, Greece, Italy, Netherlands, Portugal, Russia, Spain, Switzerland, United Kingdom, Argentina, Brazil, Chile, Colombia, Mexico, Peru, Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Tunisia, United Arab Emirates, United States, Canada, Nigeria, Kenya, South Africa, Ghana, Ethiopia

Field

Business Operations

Role type

Expert

Posted

8/4/2026

Places left

15

We wrote this page from the public micro1 listing. It may be out of date, so read the full posting before you apply.