Senior Mobile Developer
Listing checked October 7, 2026 · pay as published by Micro1
Overview
This is a contract role for a Senior Mobile Developer to help train next-generation AI systems. You will design and build Reinforcement Learning Environments that test an AI model's ability to solve complex mobile software engineering problems, such as fixing code, creating features, refactoring, and optimizing performance. Each task requires a reproducible environment and a golden reference solution. The work is remote, roughly 15 hours a week, and you can choose your preferred repositories. Compensation is output-based, paid per task that meets specifications.
What You'll Do6
- 1Create realistic, challenging mobile coding tasks for frontier AI models using repositories you select.
- 2Design problems that demand strong mobile engineering skills, such as tracking subtle bugs, building non-trivial features, handling application state and lifecycle, or improving performance.
- 3Develop tasks in your area of expertise: cross-platform apps with TypeScript, JavaScript, or Dart; iOS apps with Swift; or Android apps with Kotlin.
- 4For each task, define the problem, build a reproducible environment, and write deterministic verifiers that accept correct solutions across different valid implementations while rejecting incorrect, incomplete, or hard-coded approaches.
- 5Provide a reference solution showing how the task can be solved.
- 6Ensure each task meets project specifications and submission requirements.
Requirements7
- 1Significant hands-on expertise in at least one mobile development track: cross-platform (TypeScript or JavaScript with React Native, or Dart with Flutter), iOS (Swift with SwiftUI or UIKit), or Android (Kotlin with Jetpack Compose or the Android View system).
- 2Deep understanding of mobile application architecture, state management, asynchronous programming, and application lifecycle.
- 3Proven ability to debug complex mobile applications, resolve bugs, and deliver effective performance improvements.
- 4Experience creating reproducible environments and deterministic verifiers for coding tasks.
- 5Ability to write clear reference solutions for complex mobile problems.
- 6Strong written communication skills to document tasks and solutions.
- 7Self-directed work style with the ability to meet weekly submission requirements.
Who Should Apply
This role suits senior mobile engineers who have deep experience in at least one track (cross-platform, iOS, or Android) and enjoy crafting challenging coding problems. You should be comfortable debugging complex issues, designing reproducible environments, and writing deterministic verifiers. The role is less suitable for developers who prefer building production apps over creating training tasks, or who need close supervision. Candidates often get rejected when they submit tasks that are too easy or lack a reproducible environment, or when their verifiers accept hard-coded solutions. Another common reason for low fit scores is insufficient depth in mobile architecture and state management.
Salary Insight
Compensation is $50 to $100 per hour, paid per task that meets project specifications. The time required to complete work may vary based on your experience and workflow. Minimum weekly submission requirements apply and will be communicated during onboarding.
Pay and demand for AI Training roles
AggregatedTypical pay
$71/hour
This role
$50–$100/hr
Most AI Training roles pay $42–$100 per hour. This role's pay falls inside that range.
Based on 1,080 similar roles that publish pay · 302 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
- Live similar roles
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- Listed in last 30 days
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Hiring most right now: micro1 (362) · SME Careers (253) · Mercor (156)
Most requested skills · share of roles
- ai training13%
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- data annotation9%
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Compensation
$50–100/hr
Required Skills
Application Tip
When applying, highlight your expertise in one specific mobile track (e.g., Swift with SwiftUI or Kotlin with Jetpack Compose) and describe a complex bug you debugged or a performance improvement you delivered. Include a link to a repository that shows your work in creating reproducible environments or deterministic tests, as this directly matches the core task of building RL environments.
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