.NET Engineer | $30-$90/hr Remote
Overview
In this role, you'll apply your deep .NET expertise to help train cutting-edge AI systems. Your real-world knowledge will directly shape how models learn and reason—no prior AI experience needed, just your domain skills. You'll collaborate remotely with a global team, providing high-quality input that drives continuous improvement.
What You'll Do6
- 1Apply your in-depth understanding of .NET and C# to solve diverse technical challenges and offer domain-specific feedback.
- 2Analyze and improve AI model outputs related to the .NET, C#, AWS, and Azure ecosystems.
- 3Partner with a distributed team of experts, sharing insights to refine AI system performance.
- 4Create and refine code examples, architecture diagrams, and technical documentation to ensure high-quality data for model training.
- 5Share best practices on scalable backend development and cloud-native application design.
- 6Spot weaknesses in AI model reasoning and propose adjustments based on real-world engineering experience.
Requirements5
- 1Proven, hands-on experience building production systems with .NET and C#.
- 2Solid expertise in AWS and Azure cloud platforms, including deployment and management.
- 3Strong analytical and problem-solving skills to break down complex technical issues.
- 4Excellent communication abilities, both written and verbal, with a keen attention to detail.
- 5Comfortable working independently in a remote environment while collaborating effectively with a distributed team.
Who Should Apply
This role is perfect for a seasoned .NET developer who wants to apply their real-world experience to train next-generation AI. You're self-motivated, precise, and enjoy diving into complex technical challenges. You thrive in remote, collaborative environments and are excited about shaping how AI understands and processes technical domains.
Salary Insight
The role pays between $30 and $90 per hour, depending on experience, as a contractor position.
Required Skills
Application Tip
When applying, highlight specific examples of how you have debugged complex .NET issues or optimized cloud architectures — this demonstrates the critical thinking needed to evaluate and improve AI model outputs.
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