Micro1 is hiring for data review and workflow analysis specialists to improve AI systems. The role involves reviewing software system screenshots, analyzing UI states, validating generated outputs, and identifying inconsistencies. It requires strong attention to detail and experience in QA, data validation, technical support, or software engineering, focusing on evidence-based reasoning rather than generic data entry.
A starter prompt for Claude Code, what you'll need, and how to reach them.
You are an expert full-stack developer. Your task is to develop an MVP for an AI-assisted tool that helps 'Data Review & Workflow Analysis Specialists' identify inconsistencies in software system screenshots and validate generated outputs. The core idea is to automate the first pass of visual analysis, reducing manual effort for humans. Stack: Next.js 16 App Router (React 19), Tailwind v4, AI SDK v6 with Gemini Vision Pro, Neon Postgres on Vercel. MVP scope: 1. **Image Upload and Display**: Allow users to upload a sequence of screenshots (e.g., JPEG, PNG) representing a workflow. Display them in a clean, scrollable interface. 2. **Basic Anomaly Detection (Visual)**: Implement a feature where the AI (Gemini Vision Pro) can analyze two sequential screenshots and highlight visually significant changes or potential inconsistencies. Focus on obvious UI element shifts, missing components, or unexpected data display. 3. **Output Validation Prompt**: Provide a text area where the user can input a 'validation criterion' (e.g., 'Ensure the invoice total is $123.45'). Use Gemini to evaluate a provided screenshot against this criterion and return a 'Pass'/'Fail' with a brief explanation. 4. **Issue Documentation**: For identified anomalies or validation failures, allow the user to add a short text comment/description and tag the specific screenshot. Store these comments in the Neon Postgres database, linked to the images. Database schema: Create a `screenshots` table (id, image_url, order_in_workflow, upload_timestamp) and an `issues` table (id, screenshot_id, description, AI_detection_type, user_comment, timestamp). Build/Verify Gate: A user can upload 3-5 screenshots, run a basic visual anomaly check between pairs, input a validation prompt for one, and successfully log an issue with a comment against a specific image. Ensure the UI is responsive and clean.
Reach out to AI labs and enterprise teams using Micro1's services, offering 'agent-eval-lab' or 'forge-kit' as complementary tools for improving their internal AI development and testing workflows, which this role aims to service.
Company: micro1 Website: https://micro1.ai micro1 works with AI labs and enterprise teams to improve AI systems through evaluations, data review, reinforcement learning environments, and expert feedback workflows. We’re currently looking for reviewers with strong attention to detail and experience analyzing software systems/workflows. What the work involves: • reviewing screenshots of software systems • analyzing UI states and workflows • validating generated outputs • identifying inconsistencies between visible evidence vs assumptions • documenting issues and reasoning clearly Strong fit for people with backgrounds in: • QA/testing • OCR/data validation • annotation/review work • technical support • product operations • UX research • debugging/admin workflows • software engineering This is NOT generic data entry. The role is much more about: • evidence-based reasoning • attention to detail • interpreting workflows correctly • spotting misleading/conflicting system states Examples of relevant experience: • reviewing OCR extraction accuracy • debugging stale/fallback UI states • validating admin/dashboard workflows • identifying inconsistencies between frontend state and backend tru
Message u/Mysterious-Draw-3897 on Reddit with a direct message.
“I saw your hiring post for data review specialists. I've built a prototype of an AI-assisted tool that automates the initial screening for UI/workflow inconsistencies, designed to make your specialists more efficient. I can demonstrate how it identifies 'misleading/confusing system states' automatically, freeing up human talent for deeper analysis.”
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