Machine Learning Specialist - AI Trainer - Remote

Overview

We are seeking a Machine Learning Engineer Specialist- AI Trainer to join one of our clients. You'll play a critical role in advancing AI models by reviewing and refining outputs generated by cutting-edge large language models (LLMs). By applying your expertise in machine learning, data science, and technical domains, you'll 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.

In Short

• Evaluate and critique AI-generated outputs focusing on technical and scientific domains.
• Review complex model responses and suggest improvements.
• Contribute to the curation and refinement of datasets for AI/ML models.
• Collaborate with cross-functional AI teams to identify data patterns and model blind spots.
• Stay updated on model behaviors and guidelines.
• MS or PhD in Computer Science, Machine Learning, Data Science, or related field.
• 3+ years of experience as a Machine Learning Engineer or Data Scientist.
• Strong grasp of core machine learning concepts and evaluation strategies.
• Excellent written communication skills.
• Passion for AI development and data quality.

Requirements

• Experience with LLMs and NLP systems is highly desirable.
• Ability to assess complex technical information and provide feedback.
• Independent operation with sound judgment under ambiguous conditions.
• Publications in machine learning or AI journals are a plus.
• Experience with human feedback loops in ML systems.
• Teaching or mentoring experience in ML is nice to have.
• Exposure to generative AI applications or prompt engineering.

Benefits

• Work in a remote and distributed team.
• Opportunity to own every step of your development.
• Environment of opportunities, learning, and growth.
• Challenging projects with a team of professionals.
• Focus on customer-first mentality and urgency.
• Encouragement of ownership and respect within the team.
• Frugal and cost-conscious approach to projects.
• Commitment to raising standards and improving processes.