Machine Learning Specialist - AI Trainer

Summary

Join RYZ Labs as a Machine Learning Engineer Specialist- AI Trainer and play a critical role in advancing AI models by reviewing and refining outputs generated by LLMs. You will ensure that model outputs maintain high standards of technical accuracy, relevance, and consistency. This role combines analytical thinking with hands-on data quality work, offering research-grade insights into model evaluation and improvement. You will use internal tools to evaluate AI-generated outputs, focusing on technical and scientific domains, and collaborate with cross-functional AI teams. Staying updated on model behaviors and guidelines is crucial. The position requires a strong grasp of core machine learning concepts and experience with LLMs or applied ML. You will contribute to dataset curation and refinement for AI/ML model training and fine-tuning.

Requirements

• MS or PhD in Computer Science, Machine Learning, Data Science, or a related technical field
• Alternatively, 3+ years of professional experience as a Machine Learning Engineer or Data Scientist at a top-tier company (e.g., FAANG, leading startups, AI labs)
• Strong grasp of core machine learning concepts, model training workflows, and evaluation strategies
• Ability to assess complex technical information and provide constructive, detail-oriented feedback
• Excellent written communication skills, both for technical and explanatory writing
• Ability to operate independently with sound judgment under ambiguous conditions
• Passion for AI development, data quality, and technological advancement

Responsibilities

• Use internal tools to evaluate and critique AI-generated outputs, focusing primarily on technical and scientific domains
• Review complex model responses and suggest improvements with an emphasis on clarity, correctness, and domain relevance
• Contribute to the curation and refinement of datasets used to train and fine-tune AI/ML models
• Collaborate closely with cross-functional AI teams to identify data patterns, edge cases, and model blind spots
• Stay updated on model behaviors and guidelines as they evolve, applying sound judgment to nuanced annotation tasks

Preferred Qualifications

• Experience with LLMs, NLP systems, or applied ML in production settings is highly desirable
• Publications in machine learning, AI, or computer science journals/conferences
• Experience working with human feedback loops in ML systems (e.g., RLHF, data annotation, model alignment)
• Teaching, mentoring, or technical writing experience in ML or related technical domains
• Exposure to generative AI applications or prompt engineering