Remote AI Data Trainer for Environmental Engineering

Alignerr Corp.; Remote; GBP 80,000 - 100,000; Part time

Job summary

A leading AI research firm seeks an Environmental Engineering - AI Data Trainer to design and evaluate complex environmental problems and solutions. This role requires a Master's or PhD in Environmental Engineering or related fields. Candidates will review AI-generated plans and enhance model reasoning. The position offers competitive hourly pay, remote work flexibility, and potential for contract extension. Ideal for those with strong analytical writing and attention to detail. Prior environmental modeling familiarity preferred.

Benefits

Competitive pay and flexible remote work

Autonomy and global collaboration

Exposure to cutting-edge AI projects

Qualifications

• Strong foundational knowledge in aquatic chemistry and wastewater process design.
• Ability to communicate complex ecological and engineering concepts clearly.
• High level of precision when checking unit conversions and regulatory compliance.

Responsibilities

• Design advanced environmental engineering problems across various domains.
• Create rigorous technical solutions, including hydraulic flow models.
• Evaluate AI-generated remediation plans for accuracy and safety.
• Identify logical fallacies in AI reasoning and provide feedback.

Skills

Advanced Degree in Environmental Engineering

Domain Expertise in aquatic chemistry

Analytical Writing

Attention to Detail

Education

Masters or PhD in Environmental Engineering or related field

Job description

A leading AI research firm seeks an Environmental Engineering - AI Data Trainer to design and evaluate complex environmental problems and solutions. This role requires a Master's or PhD in Environmental Engineering or related fields. Candidates will review AI-generated plans and enhance model reasoning. The position offers competitive hourly pay, remote work flexibility, and potential for contract extension. Ideal for those with strong analytical writing and attention to detail. Prior environmental modeling familiarity preferred.