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SWEAP Engineer for Training AI Data - Job Opportunity at G2i Inc.

Dublin, Ireland
Contract
Mid-level
Posted: June 2, 2025
Remote
USD 30-50 per hour

Benefits

Flexible scheduling with ability to set your own hours and take extended breaks without traditional time tracking
Performance-based compensation structure allowing unlimited earning potential based on work volume
Remote work arrangement providing global accessibility and eliminating commute requirements
Weekly payment system ensuring consistent cash flow and financial flexibility
Long-term contract stability with guaranteed work availability for up to 2 years
Professional development opportunity to gain experience in cutting-edge AI/LLM technology without prior AI background required
Access to specialized RLHF platform and comprehensive support team for technical assistance
Project variety exposure ranging from code evaluation to full application development

Key Responsibilities

Verify and validate testing environments using comprehensive log analysis to ensure AI training data quality and reliability
Categorize complex coding problems by technical specificity and knowledge domains to optimize AI learning pathways
Create detailed annotation notes and solution guidance to enhance AI model understanding of optimal coding practices
Conduct thorough quality assessments of AI-generated code with detailed rationale documentation to improve model performance
Develop functional and efficient code solutions across multiple programming languages to establish training benchmarks
Design and implement robust test cases that verify code effectiveness and performance standards for AI training validation
Generate comprehensive instructions and conduct code reviews that directly influence AI model training outcomes
Lead diverse project initiatives from code snippet evaluation to full-scale application development using conversational AI interfaces

Requirements

Education

Bachelor's or Master's degree in Computer Science preferred but not required

Experience

Professional software engineering experience on production repositories and experience building and maintaining large-scale coding repositories

Required Skills

Complete fluency in the English language Go: concurrency management, race conditions, web server implementations Java: Thread-safe implementations, caching systems, concurrent operations TypeScript: Advanced type features, generics, utility types JavaScript: Deep object cloning, memory management, prototypal inheritance Python: Optimization techniques, algorithm implementation, recursion Ability to articulate complex technical concepts clearly and engagingly Excellent attention to detail and ability to maintain consistency in writing Solid understanding of grammar, punctuation, and style guidelines Experience with modern JavaScript frameworks and libraries (Next.js, Vue, Angular) Familiarity with frontend testing frameworks (Jest, React Testing Library, Cypress) Knowledge of state management solutions (Redux, Context API, MobX) Proven analytical skills with an ability to approach problems creatively Adept communication skills, especially when understanding and discussing project requirements
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Sauge AI Market Intelligence

Industry Trends

The AI training data annotation market is experiencing unprecedented growth as companies race to develop more sophisticated large language models, with the global AI training dataset market projected to reach $8.2 billion by 2028. This surge is driven by the increasing complexity of AI models requiring higher-quality, more nuanced training data that can only be provided by human experts with domain-specific knowledge. There is a significant shift toward Reinforcement Learning from Human Feedback (RLHF) methodologies in AI development, as seen with successful implementations in ChatGPT and Claude models. This approach requires specialized human annotators who can provide nuanced feedback on AI-generated content, creating a new category of technical roles that bridge software engineering and AI training. The democratization of AI development is creating opportunities for software engineers to transition into AI-adjacent roles without requiring traditional machine learning backgrounds. Companies are prioritizing practical coding experience over theoretical AI knowledge, recognizing that domain expertise in software development is crucial for training models that generate high-quality code. Remote work has become the standard for AI training annotation roles due to the global talent shortage and the need to access diverse perspectives from different geographic regions. This trend is particularly strong in the AI sector where companies are willing to pay premium rates for quality annotation work regardless of location.

Salary Evaluation

The hourly rate of $30-50 represents competitive compensation in the AI training annotation space, translating to approximately $62,400-$104,000 annually for full-time equivalent work. This rate is above average for contract technical writing roles but reflects the specialized nature of AI training data creation and the current market demand for experienced software engineers in this emerging field.

Role Significance

Typical team structures for AI annotation projects range from 10-50 annotators working under 2-3 project leads, with this role likely being part of a distributed team of specialized contributors rather than a traditional hierarchical engineering team structure.
This role represents a mid-level position that combines senior technical expertise with emerging AI specialization, offering experienced software engineers an entry point into the rapidly growing AI training sector while leveraging their existing development skills rather than requiring extensive retraining.

Key Projects

Large-scale code generation model training requiring thousands of annotated code examples across multiple programming languages and complexity levels Development of AI coding assistants that can understand and generate context-appropriate solutions for real-world GitHub issues and pull requests Creation of comprehensive testing frameworks that can evaluate AI-generated code quality and functionality across diverse software engineering scenarios Multi-language code optimization projects where AI models learn to refactor and improve existing codebases while maintaining functionality and readability

Success Factors

Deep technical expertise across multiple programming languages combined with the ability to articulate complex concepts clearly, as this role requires both solving challenging coding problems and explaining the reasoning behind optimal solutions in a way that AI models can learn from effectively. Strong analytical and pattern recognition skills to identify the nuances that distinguish high-quality code solutions from mediocre ones, enabling the creation of training data that helps AI models understand not just what works, but what represents best practices in software development. Adaptability and continuous learning mindset given the rapidly evolving nature of AI technology and training methodologies, requiring professionals to stay current with both software engineering best practices and emerging AI training techniques. Excellent written communication skills and attention to detail, as the quality of annotations and explanations directly impacts the effectiveness of AI model training and requires consistency across large volumes of work.

Market Demand

High demand with significant growth trajectory as the AI industry continues expanding and requiring more sophisticated training methodologies

Important Skills

Critical Skills

Advanced proficiency in Go, Java, JavaScript, Python, and TypeScript is essential as these languages represent the primary focus areas for current AI model training initiatives, with specific emphasis on complex concepts like concurrency, memory management, and type systems that are challenging for AI models to master independently. Professional experience with production-scale repositories and large codebase maintenance is crucial because the role requires understanding of real-world software engineering challenges that go beyond academic or tutorial-level programming, enabling the creation of training data that reflects actual industry practices and constraints. Technical writing and communication skills are fundamental to success as the primary deliverable is high-quality annotations and explanations that must be clear, consistent, and pedagogically effective for AI model training, requiring the ability to break down complex technical concepts into learnable components.

Beneficial Skills

Experience with modern frontend frameworks and testing methodologies provides additional value as the AI models being trained will likely need to understand full-stack development practices and contemporary software architecture patterns. Background in technical documentation and code review processes enhances the ability to create comprehensive training materials and evaluate code quality effectively, skills that directly translate to better AI training outcomes. Understanding of software optimization techniques and algorithmic thinking supports the creation of training examples that emphasize not just functional correctness but also performance and efficiency considerations that are important for professional-grade AI-generated code.

Unique Aspects

This role offers a rare opportunity to directly influence the development of next-generation AI coding assistants without requiring traditional machine learning expertise, making it accessible to experienced software engineers looking to transition into the AI sector
The combination of unlimited earning potential through hourly work with long-term contract stability (2+ years) provides both financial flexibility and career security rarely found in contract positions
Direct involvement in training some of the world's most advanced AI models provides exposure to cutting-edge technology and methodologies that will likely define the future of software development
The role bridges traditional software engineering with AI development, positioning professionals at the intersection of two critical technology domains and providing valuable cross-functional expertise

Career Growth

Career progression typically occurs within 18-24 months for high performers, with opportunities to advance to leadership roles or transition to core ML engineering positions as the AI industry continues rapid expansion

Potential Next Roles

AI Training Lead or Senior Annotation Specialist roles overseeing teams of contributors and establishing quality standards for training data creation Machine Learning Engineer positions focusing on model fine-tuning and RLHF implementation, leveraging the deep understanding of training data quality gained in this role Technical Product Manager roles at AI companies, using firsthand experience with model training challenges to guide product development and feature prioritization AI Ethics and Safety Specialist positions, applying knowledge of model training processes to identify and mitigate potential risks in AI system development

Company Overview

G2i Inc.

G2i Inc. operates as a specialized talent platform and consulting firm focused on connecting skilled developers with innovative technology companies, particularly in the emerging AI and blockchain sectors. The company has established itself as an intermediary between cutting-edge technology projects and experienced software engineers, facilitating contract-based engagements that allow professionals to work on advanced projects without traditional full-time employment constraints.

G2i occupies a niche position in the technical recruiting and project staffing market, differentiating itself by focusing on high-skill, high-compensation contract opportunities in emerging technology sectors rather than competing in the broader general staffing market.
While the specific role is listed in Dublin, G2i operates with a global remote-first approach, accepting candidates from multiple continents and leveraging international talent pools to serve clients worldwide, indicating strong capability in managing distributed workforce arrangements.
The company culture emphasizes flexibility and results-oriented work arrangements, as evidenced by their unlimited hours policy, flexible scheduling, and project-based compensation structure, suggesting an environment that attracts self-motivated professionals who prefer autonomy over traditional corporate structures.
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