ML Engineer Specialist AI Tutor, Handshake

• MS or PhD in Computer Science, Machine Learning, Data Science, or a closely related technical field
• Alternatively, 3+ years of professional experience as a Machine Learning Engineer or Data Scientist at a high-caliber company (e.g., FAANG, leading startups, AI labs)
• Strong grasp of core machine learning concepts, model training workflows, and evaluation strategies
• Experience with LLMs, NLP systems, or applied ML in production settings is highly desirable
• Ability to assess complex technical information and provide constructive, detail-oriented feedback
• Excellent written communication skills—both technical and explanatory writing
• Ability to operate independently with sound judgment under ambiguous conditions
• Passion for AI development, data quality, and technological advancement
• Bonus areas of expertise
• Availability to work evenings and weekends as needed
• Ability to quickly adapt to new skills and evolving requirements
• Personal device must support Windows 10 or macOS Big Sur 11.0 or later
• Reliable access to a smartphone

Desirable

• As an AI Tutor, Machine Learning Engineer Specialist, you will contribute to the next generation of AI models by reviewing and refining outputs generated by cutting-edge large language models (LLMs)
• You'll apply your expertise in machine learning, data science, and technical domains to ensure model outputs meet high standards of technical accuracy, relevance, and consistency
• This role blends analytical thinking with hands-on data quality work and research-grade insight into model evaluation and improvement
• This is a contract remote position with variable time commitments
• The services to be provided by the Contractor under this agreement are supplementary and not integral to the Handshake’s primary business operations
• The Contractor’s services are distinct and separate from the core business activities of Handshake
• Use internal tools to evaluate and critique AI-generated outputs, primarily in technical and scientific domains
• Review complex model responses and suggest improvements with a focus on clarity, correctness, and domain relevance
• Contribute to the curation and refinement of datasets used to train and fine-tune AI/ML models
• Work closely with cross-functional AI teams to identify data patterns, edge cases, and model blind spots
• Stay up-to-date on model behaviors and guidelines as they evolve, applying judgment to nuanced annotation tasks