AI Quality Evaluator - STEM Expert Remote

Planet Pharma; Greater London; Remote; GBP 60,000 - 75,000; Full time

Job summary

Planet Pharma seeks experienced STEM professionals to support AI coding and response evaluation across scientific and engineering disciplines. Remote work with flexible scheduling and no fixed hours for project-based assignments.

The role requires real-world industry or advanced research experience to assess accuracy, quality, and practicality of AI outputs, with emphasis on actionable feedback and domain-aligned judgment.

Qualifications

• Five+ years of substantive applied experience in a STEM field.
• Experience evaluating AI-generated responses in scientific/engineering contexts.
• Ability to assess accuracy, methodology, and practicality of AI outputs.

Responsibilities

• Evaluate AI-generated responses within your scientific/engineering area.
• Assess technical accuracy, completeness, and reasoning of outputs.
• Review analyses, engineering solutions, calculations, methods, and explanations.
• Compare AI responses and select best-in-class solutions.
• Identify inaccuracies, flaws, and unsupported conclusions.
• Provide clear written feedback and improvement suggestions.
• Apply domain expertise to ensure alignment with principles.
• Collaborate with guidelines while working remotely.

Skills

Mechanical Eng

Electrical Eng

Civil Eng

Chemical Eng

Materials Science

Environmental Eng

Energy Eng

Physics

Applied Sciences

Other Disciplines

Education

Master's or PhD

Tools

MATLAB

Python

RSolidWorks

AutoCAD

SPICE

GIS platforms

Chromatography software

Spectroscopy software

Job description

Planet Pharma seeks experienced STEM professionals to support AI coding and response evaluation across scientific and engineering disciplines. Remote work with flexible scheduling and no fixed hours for project-based assignments.

The role requires real-world industry or advanced research experience to assess accuracy, quality, and practicality of AI outputs, with emphasis on actionable feedback and domain-aligned judgment.