Remote C# AI Training Specialist

Rise Data Labs

Australia

Remote

AUD 90,000 - 140,000

Full time

4 days ago
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Benefits offered by this job

Competitive compensation
Career development opportunities
Access to cutting-edge AI tools

Job summary

Rise Data Labs, a leading AI training and data annotation company, is seeking a C# AI Training Specialist in Australia. You will annotate and label data to train AI models, validate AI-generated content for quality, and apply domain insights to improve model performance across diverse projects.

The ideal candidate has strong C# skills, a solid grasp of AI/ML concepts, and a meticulous approach to data handling.

Qualifications

  • Strong proficiency in C# and object-oriented programming.
  • Solid understanding of AI concepts and ML fundamentals.
  • Attention to detail and commitment to data quality.
  • Excellent analytical and problem-solving skills.
  • Ability to work independently and in a team.

Responsibilities

  • Train and improve AI models through precise data annotation and labeling.
  • Review and validate AI-generated content for accuracy and relevance.
  • Provide domain expertise to enhance AI model performance across various projects.
  • Collaborate with cross-functional teams to deliver high-quality data for machine learning applications.
  • Engage in continuous learning to stay updated on the latest developments in AI and C# technologies.

Skills

C# programming
AI concepts
Attention to detail
Analytical skills
Teamwork & independence

Tools

AI training frameworks
Data annotation platforms
Software development methodologies
Data privacy & ethics in AI

Job description

Rise Data Labs, a leading AI training and data annotation company, is seeking a C# AI Training Specialist in Australia. You will annotate and label data to train AI models, validate AI-generated content for quality, and apply domain insights to improve model performance across diverse projects.

The ideal candidate has strong C# skills, a solid grasp of AI/ML concepts, and a meticulous approach to data handling.

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