Remote AI Training Specialist: Mechanical Eng. Expert

Risedatalabs; EE. UU.; A distancia; USD 60.000 - 100.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive compensation

Flexible working hours

Opportunities for professional growth

Descripción de la vacante

Rise Data Labs is seeking an AI Training Specialist with a Mechanical Engineering background to annotate, label, and validate data for AI models. You will leverage domain knowledge to ensure accuracy and relevance across projects, driving model performance.

The role emphasizes data quality, collaboration with cross-functional teams, and ensuring robust training data for machine learning applications in a remote-first environment.

Formación

• Bachelor's degree in Mechanical Engineering or related field.
• Strong understanding of mechanical engineering principles.
• Experience with data annotation and AI training processes.
• Excellent attention to detail and quality assurance skills.
• Proficient in using annotation tools and software.

Responsabilidades

• Train and improve AI models by annotating and labeling data specific to mechanical engineering.
• Review and validate AI-generated content for accuracy and domain relevance.
• Provide expert insights to enhance AI model performance in the mechanical engineering field.
• Collaborate with cross-functional teams on diverse AI training projects.
• Ensure high-quality data for machine learning applications to drive model effectiveness.

Conocimientos

Attention to detail

Data annotation & AI training

Mechanical engineering knowledge

Annotation tools proficiency

Educación

Bachelor's degree in Mechanical Engineering or related field

Herramientas

Annotation tools

Descripción del empleo

Rise Data Labs is seeking an AI Training Specialist with a Mechanical Engineering background to annotate, label, and validate data for AI models. You will leverage domain knowledge to ensure accuracy and relevance across projects, driving model performance.

The role emphasizes data quality, collaboration with cross-functional teams, and ensuring robust training data for machine learning applications in a remote-first environment.