Dart QA Lead — Remote US Contract Up to $65/hr

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We help professionals discover specialized projects, build a unified portfolio, and grow long-term freelance careers teaching AI — all from one place.

OpenTrain directly hires contractors for training and evaluation work. This role is part of our mission to improve how AI models learn from human expertise.

Why AI training matters (and why this role is important)

AI systems learn from labeled examples and human feedback. Reviewing and improving those examples ensures models produce correct, safe, and useful code.

As a Dart QA Lead you’ll shape how Dart and Flutter coding examples are judged and taught to models, raising overall quality and reducing recurrent errors across training data.

The role — high level

You will lead QA for Dart and Flutter training work: reviewing AI-generated code, evaluating trainer and QA outputs, maintaining Dart-specific guidelines, and supporting contributor onboarding and quality improvement.

This is a remote contractor position limited to workers in the United States, targeted at experienced Dart/Flutter engineers and technical leads who enjoy high-signal technical feedback and documentation work.

• Location: Remote — United States only
• Hours: 20+ hours/week (part-time contractor)
• Pay: Up to $65/hour
• Employment type: Contractor, part-time
• Language: English required

What you'll do (key responsibilities)

• Review Dart code, Flutter snippets, debugging responses, tests, and explanatory text for correctness and production-readiness.
• Evaluate trainer and QA work against project rubrics and Dart-specific guidelines.
• Provide clear, detailed written feedback on syntax, null safety, async behavior, types, packages, testing, and architecture.
• Flag insecure, non-executable, misleading, or non-production-ready Dart/Flutter recommendations.
• Create and maintain documentation: style guides, examples, trackers, FAQs, honeypots, and onboarding materials.
• Support onboarding and live training or calibration calls for Dart contributors.
• Communicate guideline changes and help close recurring QA gaps and contributor activation follow-ups.

Requirements

• Strong, practical knowledge of Dart and Flutter: syntax, null safety, classes, mixins, extensions, generics, and collections.
• Deep understanding of async in Dart: futures, streams, isolates, and common async pitfalls.
• Skilled at spotting type errors, weak null-safety usage, flawed logic, hallucinated APIs, and incomplete explanations.
• Familiarity with widgets, state management patterns, pub.dev packages, unit/widget/integration testing, and mobile app architecture.
• Experience with GitHub and CI/CD workflows for mobile apps.
• Excellent written English for concise, actionable technical feedback.
• Experience leading or supporting remote teams of trainers, engineers, reviewers, or QAs is strongly preferred.

Helpful background and tools

The following are advantageous but not strictly required if you can demonstrably meet the core requirements.

• Bachelor’s or Master’s in CS, Software Engineering, or equivalent professional experience.
• Experience with AI training, LLM evaluation, code QA, or rubric-based code review is a strong plus.
• Comfort using Discord, Google Sheets, Google Docs, trackers, dashboards, and common PM systems.
• Highly organized: able to maintain style guides, trackers, calibration tasks, and onboarding materials.

How this works and next steps

OpenTrain hires contractors to perform reviewer and QA work directly. If this role fits your skills, create or use your OpenTrain account to apply and build a profile showing relevant Dart/Flutter experience.

Successful applicants will be invited to onboarding and calibration tasks to align on rubrics and quality targets before starting paid work.

• Work will involve RLHF-style evaluation and code review tasks focused on Dart/Flutter.
• Compensation is hourly (up to $65/hr) and paid according to contractor terms.