AI Work Central

Dart Team Lead

SME CareersCodingFirst seen 9 Oct 2026

up to$65/hr as listed by SME Careers, USDEstimate a week at this rate
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Where
United States
Language
English
Type
Contract, remote
Field
Coding

Posted on SME Careers First seen 9 Oct 2026Checked 12 Oct 2026

From the SME Careers listing

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.

Text above is the platform's own listing, shown as published. Check the details on SME Careers before applying.

Prepare for the assessment

Practice questions and what each platform says about its screening for this kind of role:

Terms in this listing

  • Guidelines: The project's written instructions that define how to do a task and how to judge answers. On most projects they take precedence over personal preference.
  • Instruction following: Whether a model did exactly what was asked, including constraints such as length, format, language or things to avoid.
  • Rubric: A written list of criteria and scores used to judge a response, for example accuracy, instruction following and tone, each with clear pass or fail descriptions.
  • AI interview: A screening interview run by an AI system instead of a person, usually spoken or on video, with questions about your background and skills.
  • Reviewer: A more experienced worker who checks other people's tasks, gives feedback and scores quality.

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