Electrical Engineering Specialist - AI Trainer

Summary

Join RYZ Labs as an Electrical Engineering Specialist - AI Trainer and play a crucial role in refining AI models. You will review and correct AI-generated content on electrical engineering topics, ensuring accuracy and technical precision. Analyze model responses, annotate training data, and collaborate with technical teams to identify and correct inaccuracies. Apply your engineering judgment to maintain high-quality model outputs. This role requires a PhD or MS in Electrical Engineering or related field, or 3+ years of relevant experience. Deep understanding of core electrical engineering principles and strong written communication skills are essential. RYZ Labs offers a remote and distributed work environment with opportunities for growth and development.

Requirements

• PhD or MS in Electrical Engineering or a related field, or 3+ years of professional experience as an Electrical Engineer, Embedded Systems Engineer, or similar
• Deep understanding of core electrical engineering principles, system design, and signal analysis
• Ability to assess complex technical material and provide constructive feedback
• Strong written communication skills, especially in explaining technical concepts clearly
• Independent and detail-oriented, with strong decision-making skills

Responsibilities

• Analyze and improve AI-generated content in electrical and electronic engineering domains
• Evaluate model responses for accuracy in areas such as analog/digital circuits, control systems, communications, and microelectronics
• Annotate and curate training data for electrical engineering-related LLM outputs
• Collaborate with technical teams to identify gaps, inaccuracies, or misleading content
• Apply engineering judgment to ensure high-quality, consistent model outputs

Preferred Qualifications

• Experience with simulation tools (e.g., SPICE), PCB design, or FPGA programming
• Familiarity with control theory, power electronics, or IoT systems
• Prior teaching, tutoring, or curriculum development in electrical engineering
• Exposure to LLMs or AI-based educational tools
• Participation in model evaluation or annotation workflows