Remote Synthetic Chemistry AI Trainer

BAM Ventures; New York (NY); Remote; USD 34,000 - 55,000; Full time

Benefits offered by this job

Competitive hourly rates

Flexible working hours

Professional development opportunities

Job summary

Rise Data Labs is seeking a Synthetic Chemistry AI Trainer Specialist to help enhance AI models through precise data annotation and validation in the synthetic chemistry domain. You will collaborate with a multidisciplinary team in a remote-friendly environment.

You will annotate and label data, review AI outputs for accuracy, and apply your domain expertise to improve model performance while ensuring data integrity across projects in AI training and data science.

Qualifications

• Bachelor’s degree in Chemistry, Chemical Engineering, or related field.
• Proven experience in synthetic chemistry with a solid grasp of chemical processes.
• Familiarity with AI technologies and data annotation tools.
• Exceptional attention to detail and accuracy in data handling.
• Strong analytical skills and ability to work independently.

Responsibilities

• Train and improve AI models through comprehensive data annotation and labeling.
• Review and validate AI-generated content for accuracy in synthetic chemistry.
• Provide domain expertise to enhance AI model performance and data integrity.
• Engage in AI training projects across disciplines, contributing to various applications.
• Maintain high-quality data for ML applications through rigorous QA standards.

Skills

Synthetic chemistry

Attention to detail

Analytical skills

Independent work

Familiar with AI technologies

Education

Bachelor’s degree in Chemistry or related field

Tools

AI data annotation tools

Job description

Rise Data Labs is seeking a Synthetic Chemistry AI Trainer Specialist to help enhance AI models through precise data annotation and validation in the synthetic chemistry domain. You will collaborate with a multidisciplinary team in a remote-friendly environment.

You will annotate and label data, review AI outputs for accuracy, and apply your domain expertise to improve model performance while ensuring data integrity across projects in AI training and data science.