Remote Python QA Coder for AI Code Training

Outlier

Región Centro

À distance

MXN 718 000 - 1 023 000

Plein temps

Il y a 2 jours
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Avantages offerts par ce poste

Remote work across LatAm
Missions incentive program
Free access to leading LLMs ($200+/mo)

Résumé du poste

Outlier invites experienced software engineers to join a remote QA coding program across Latin America. Train AI models by reviewing generated code, scoring model responses, and crafting CS prompts. No AI experience required; prior software engineering and clear English communication are essential.

Compensation is performance-based, with core project work paying up to $34 USD/hr plus incentives. Flexible, remote work from anywhere in LatAm, with access to leading LLMs and a structured onboarding

Qualifications

  • Bachelor's degree or higher in Computer Science.
  • Software engineering experience contributing to a live production codebase.
  • Ability to articulate complex concepts fluently in written and spoken English.
  • Excellent attention to detail, including grammar, punctuation, and style guidelines.

Responsabilités

  • Review AI-generated code and flag errors, inconsistencies, or quality issues using structured guidelines.
  • Rate and rank model responses across key dimensions — accuracy, instruction following, and presentation.
  • Write computer science questions and develop clear, well-structured prompts to challenge AI models.

Connaissances

Software engineering
English fluency
Attention to detail

Formation

Bachelor's degree in Computer Science

Description du poste

Outlier invites experienced software engineers to join a remote QA coding program across Latin America. Train AI models by reviewing generated code, scoring model responses, and crafting CS prompts. No AI experience required; prior software engineering and clear English communication are essential.

Compensation is performance-based, with core project work paying up to $34 USD/hr plus incentives. Flexible, remote work from anywhere in LatAm, with access to leading LLMs and a structured onboarding

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