Kotlin Engineer - Spain

As a remote, hourly paid Kotlin Engineer, you will review AI-generated responses and generate high-quality Kotlin-focused content, evaluating the reasoning quality and step-by-step problem-solving behind each solution. You will assess solutions for accuracy, clarity, and adherence to the prompt; identify errors in methodology or conceptual understanding; fact-check technical information; craft expert-level explanations and model solutions that demonstrate correct approaches; and rate and compare multiple AI responses based on correctness and reasoning quality. T his role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate that provides AI training data for many of the world’s largest AI companies and foundation model labs, helping to improve the world’s premier AI models.

### Your Profile
- 2+ years of hands-on Kotlin development experience.
- Strong understanding of Kotlin null safety, coroutines, and functional idioms.
- Professional software engineering experience using Kotlin for Android or backend services.
- Minimum Bachelor’s degree in Computer Science or a closely related field.
- Ability to critically evaluate idiomatic Kotlin style, design decisions, and architecture trade-offs.
- Significant experience using large language models (LLMs) to assist with coding, debugging, and code review.
- Excellent English writing skills with the ability to articulate complex technical concepts clearly.
- Highly preferred: Experience with Ktor, Android Jetpack, and advanced coroutine usage in production systems.
- Preferred: Merged pull requests in Kotlin open-source projects, competitive programming experience, or similar demonstration of strong problem-solving skills.
- Experience reviewing app logic or mobile/backend architecture, with previous AI data training or model evaluation work strongly preferred and Minimum C1 English proficiency.

### Key Responsibilities
- Develop AI Training Content: Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects.
- Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance.
- Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases.