Senior C Engineer for AI Model Training

Remote
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AI Summary

Review AI-generated C code, generate high-quality reference implementations, and assess solutions for accuracy and safety. Requires expertise in C, systems programming, and low-level debugging. Work remotely on an hourly paid basis.

Key Highlights
Review AI-generated C code
Generate high-quality reference implementations
Assess solutions for accuracy and safety
Key Responsibilities
Develop AI Training Content
Optimize AI Performance
Ensure Model Integrity
Technical Skills Required
C Systems programming Low-level debugging Concurrency Operating systems Hardware interaction Performance optimization Build systems Compilers Linkers Debugging tools
Benefits & Perks
Hourly paid basis
Remote work
Opportunity to improve world's premier AI models
Nice to Have
Previous experience with AI data training, annotation, or evaluating AI-generated technical content

Job Description


Company Description

AI Data Training Hub specializes in connecting subject-matter experts with remote opportunities to train and evaluate AI models, enabling the creation of high-quality AI solutions across various domains. Focused on bridging the gap between human expertise and advanced machine learning, the organization empowers professionals in STEM, Legal, Medical, and Creative fields to shape the future of AI innovation. By fostering collaboration, AI Data Training Hub ensures the development and refinement of next-generation artificial intelligence technologies.


Role Description

As a Senior C Engineer, you will work remotely on an hourly paid basis to review AI-generated C code, low-level systems designs, and technical explanations, as well as generate high-quality reference implementations and step-by-step reasoning for complex engineering problems. You will assess solutions for accuracy, clarity, safety, and adherence to the prompt; identify errors in logic, memory handling, concurrency, or performance; fact-check technical information; write high-quality explanations and model solutions that demonstrate correct methods; and rate and compare multiple AI responses based on correctness and reasoning quality. This fully remote, hourly paid contractor role is with SME Careers, a fast-growing AI data services company and subsidiary of SuperAnnotate that provides AI training data to many of the world’s largest AI companies and foundation model labs. Your systems-level C expertise will directly help improve the world’s premier AI models used by millions of developers worldwide.


Your Profile
  • Bachelor’s degree or higher in Computer Science, Electrical/Computer Engineering, or a closely related technical field.
  • 4+ years of professional experience developing production software in C for systems, embedded, or performance-critical applications.
  • Expert-level proficiency in C, including deep understanding of pointers, memory management, undefined behavior, and low-level debugging.
  • Strong background in systems programming concepts such as concurrency, operating systems, hardware interaction, and performance optimization.
  • Hands-on experience with build systems, compilers, linkers, and debugging tools commonly used in C development.
  • Minimum C1 English proficiency (written and spoken), with the ability to write clear technical explanations and follow detailed English-language guidelines.
  • Proven experience conducting detailed code reviews and enforcing coding standards for safety and maintainability in C codebases.
  • Comfort working with version control, CI/CD workflows, and automated testing for C projects.
  • Previous experience with AI data training, annotation, or evaluating AI-generated technical content is a strong plus.
  • Highly detail-oriented and systematic, with a methodical approach to evaluating reasoning quality and identifying subtle issues in low-level code.


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.

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