Chemistry AI Benchmark Specialist (Remote)

Alignerr Corp.; Sheffield (TX); Remote; USD 83,000 - 165,000; Part time

Benefits offered by this job

Fully remote

Flexible schedule

Contract renewal potential

Global collaboration

Job summary

Alignerr seeks a Chemistry Subject Matter Expert (AI Training) to design challenging chemistry problems and craft precise solutions for evaluating AI reasoning. This fully remote, hourly contract role offers 10–40 hours per week and collaboration with world-leading AI research teams.

You will benchmark AI performance across chemistry domains and provide actionable feedback to improve models, with emphasis on accuracy and structured reasoning.

Qualifications

• Pursuing or completed a Master’s in Chemistry, Chemical Engineering, Biochemistry, or related field.
• Excellent problem-solving across advanced chemistry domains.
• Ability to explain complex concepts in writing with precision.
• Detail-oriented with systematic evaluation of reasoning.
• Self-motivated, able to work independently.

Responsibilities

• Design rigorous, advanced chemistry problems to challenge and benchmark AI performance across domains.
• Develop clear, step-by-step solutions with precise, logical reasoning.
• Evaluate AI-generated outputs for scientific accuracy, depth, and quality of reasoning.
• Collaborate with researchers to refine and improve benchmarks spanning undergrad to Master’s level chemistry.
• Provide structured, actionable feedback to help AI models improve over time.

Skills

Strong problem-solving

Clear writing

Independent work

Asynchronous collaboration

Education

Master's degree in Chemistry or related field

Job description

Alignerr seeks a Chemistry Subject Matter Expert (AI Training) to design challenging chemistry problems and craft precise solutions for evaluating AI reasoning. This fully remote, hourly contract role offers 10–40 hours per week and collaboration with world-leading AI research teams.

You will benchmark AI performance across chemistry domains and provide actionable feedback to improve models, with emphasis on accuracy and structured reasoning.