Dart Team Lead

SME Careers; United States; Remote; USD 62,000 - 117,000; Part time

Job summary

SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, seeks a Dart Quality Assurance Lead (QAL) for an hourly remote contractor role.

You will review AI-generated Dart code, assess quality against guidelines, and provide precise feedback to ensure accuracy, readability, and maintainability across training projects.

Selection includes an AI interview, a domain-specific task, and a recruiter interview; future opportunities may arise as opportunities expand.

Qualifications

• Bachelor's or Master's degree in Computer Science, Software Engineering, IT, or equivalent professional experience.
• Strong grasp of English to follow guidelines and provide clear technical feedback.
• 3+ years of professional experience in Dart, Flutter, mobile development, frontend development, code review, QA, or technical mentoring.
• Strong understanding of Dart syntax, null safety, classes, mixins, extensions, generics, collections, futures, streams, isolates, packages, and error handling.
• Ability to identify issues such as incorrect async handling, type errors, weak null-safety usage, non-executable code, flawed logic, or hallucinated APIs.
• Familiarity with Flutter, widgets, state management, pub.dev packages, unit/widget testing, integration testing, build tools, mobile app architecture, GitHub, and CI/CD is preferred.
• Experience leading or supporting remote teams of trainers, engineers, reviewers, or QAs is strongly preferred.
• Comfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and PM systems.
• Experience with AI training, LLM evaluation, code QA, or rubric-based code review is a strong plus.

Responsibilities

• Spot-check Dart items and provide ongoing feedback through DMs.
• Review AI-generated Dart code, Flutter-related snippets, debugging responses, tests, and explanations.
• Communicate guideline changes and Dart-specific standards on Discord.
• Answer questions around Dart syntax, null safety, async behavior, Flutter usage, packages, tests, and rubric interpretation.
• DM inactive contributors and manage activation follow-ups.
• Create Dart documentation, style guides, examples, trackers, FAQs, honeypots, and onboarding materials.
• Run onboarding/training calls for Dart contributors.
• Flag insecure, misleading, non-executable, or non-production-ready Dart/Flutter recommendations.
• Improve QA processes based on recurring gaps.

Skills

Dart

Flutter

Code review

QA

Technical mentoring

Remote team leadership

English communication

Null safety

CI/CD

Frontend development

Education

Bachelor's or Master's degree in Computer Science/Software Engineering/IT

Tools

GitHub

Discord

Google Sheets

Google Docs

Job description

In this hourly, remote contractor role, you will work as a Dart Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across Dart AI training projects. You will review AI-generated Dart code and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure contributors follow expected standards.

You will assess work for code correctness, type safety, async behavior, Flutter awareness, debugging accuracy, readability, maintainability, performance, test coverage, formatting, instruction-following, and rubric adherence.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world's largest AI companies and foundation-model labs. Your Dart quality leadership will help ensure Dart training data is accurate, executable, maintainable, clearly explained, and aligned with client expectations.

Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.

Important:
There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

Responsibilities

• Spot-check Dart items and provide ongoing feedback through DMs.
• Review AI-generated Dart code, Flutter-related snippets, debugging responses, tests, and explanations.
• Communicate guideline changes and Dart-specific standards on Discord.
• Answer questions around Dart syntax, null safety, async behavior, Flutter usage, packages, tests, and rubric interpretation.
• DM inactive contributors and manage activation follow-ups.
• Create Dart documentation, style guides, examples, trackers, FAQs, honeypots, and onboarding materials.
• Run onboarding/training calls for Dart contributors.
• Flag insecure, misleading, non-executable, or non-production-ready Dart/Flutter recommendations.
• Improve QA processes based on recurring gaps.

Requirements

• Bachelor's or Master's degree in Computer Science, Software Engineering, IT, or equivalent professional experience.
• Strong grasp of English to follow guidelines and provide clear technical feedback.
• 3+ years of professional experience in Dart, Flutter, mobile development, frontend development, code review, QA, or technical mentoring.
• Strong understanding of Dart syntax, null safety, classes, mixins, extensions, generics, collections, futures, streams, isolates, packages, and error handling.
• Ability to identify issues such as incorrect async handling, type errors, weak null-safety usage, non-executable code, flawed logic, hallucinated APIs, or incomplete explanations.
• Familiarity with Flutter, widgets, state management, pub.dev packages, unit/widget testing, integration testing, build tools, mobile app architecture, GitHub, and CI/CD is preferred.
• Experience leading or supporting remote teams of trainers, engineers, reviewers, or QAs is strongly preferred.
• Comfortable with Discord, Google Sheets, Google Docs, trackers, dashboards, GitHub, and PM systems.
• Highly organized and able to maintain style guides, trackers, FAQs, honeypots, calibration tasks, and onboarding materials.
• Experience with AI training, LLM evaluation, code QA, or rubric-based code review is a strong plus.