Senior Mobile Engineer (Remote)

Joblet-AI; India; Remote; INR 1,653,000 - 3,306,000; Part time

Benefits offered by this job

Remote work

Flexible schedule

Paid on task

Job summary

Joblet-AI is seeking a Senior Mobile Software Engineer (Contractor) to craft reinforcement learning environments for mobile coding tasks. Remote, ~15 hours per week. You’ll define problems, build reproducible environments, and provide deterministic verifiers and a reference solution.

Strong mobile engineering skills in iOS or Android are preferred, with deep knowledge of architecture, state management, and lifecycle. Timeline is immediate; you’ll start within 24–48 hours of onboarding.

Qualifications

• Significant hands-on mobile development experience with modern architectures.
• Strong understanding of app lifecycle, state management, and asynchronous programming.
• Proven ability to debug complex mobile apps and deliver performance improvements.

Responsibilities

• Create realistic, challenging mobile coding tasks for frontier AI models.
• Design problems requiring bug fixing, feature development, and performance optimization.
• Define reproducible environments and write deterministic verifiers and reference solutions.

Skills

Mobile development

Swift (iOS)

Kotlin (Android)

Debugging complex apps

Performance optimization

Tools

Xcode

Android Studio

Job description

• Role Title: Senior Mobile Software Engineer
• Role Type: Contractor (~15 hrs a week)
• Location: Remote

In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.

As an expert, you will create Reinforcement Learning Environments that test an AI model’s ability to solve complex mobile software engineering problems related to fixing code, creating features, refactoring code, and optimizing performance. You will be tasked with creating a reproducible environment and a golden reference solution for each problem.

Scope of Work

1. Create realistic, interesting, and extremely challenging mobile coding tasks for frontier AI models using your preferred repositories.

2. Design problems that require strong mobile engineering skills, such as tracking down subtle bugs, building non-trivial features, handling application state and lifecycle, or improving performance.

4. For 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.

5. Provide a reference solution showing how the task can be solved.

Preferred Qualifications

1. Significant hands-on expertise in at least one of the following mobile development tracks:

3. iOS: Swift with SwiftUI or UIKit.

4. Android: Kotlin with Jetpack Compose or the Android View system.

5. Deep understanding of mobile application architecture, state management, asynchronous programming, and application lifecycle.

6. Proven ability to debug complex mobile applications, resolve bugs, and deliver effective performance improvements.

Compensation Structure

Compensation is output-based; experts are paid per task that meets the project specifications. The time required to complete work may vary depending on the expert’s experience and workflow. Minimum weekly submission requirements apply and will be communicated during onboarding.

Start Timeline & Availability

1. We typically fill roles within 48 hours and are looking for experts ready to jump in right away. If selected, we expect you to start your first tasks within 24–48 hours of completing onboarding.

What You’ll Do

1. Create realistic, interesting, and extremely challenging mobile coding tasks for frontier AI models using your preferred repositories.

2. Design problems that require strong mobile engineering skills, such as tracking down subtle bugs, building non-trivial features, handling application state and lifecycle, or improving performance.

4. For 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.

5. Provide a reference solution showing how the task can be solved.