You’ll ensure annotation teams maintain high standards for instruction-following, correctness, and functionality in AI training datasets.
Why Join Us?
Work at the intersection of AI and software development.
Remote-first culture with flexible arrangements.
Competitive hourly compensation.
Early-stage pipeline project with long-term potential.
Responsibilities
Review and audit annotator evaluations of AI-generated Python code.
Validate code functionality, security, and best practices across multiple programming Languages.
Execute proof-of-work methodology using Docker-based environments.
Provide constructive feedback to maintain annotation quality standards.
Work within established guidelines for evaluation consistency.
Required skills
Strong Python expertise, including syntax, debugging, and testing.
Docker proficiency for environment isolation and testing.
Excellent written communication and documentation skills.
Experience with structured QA or annotation workflows.
Multiple coding languages required (list provided separately).
Preferred skills
Experience in AI training, LLM evaluation, or model alignment.
Familiarity with RLHF pipelines and annotation platforms.
Advanced Docker knowledge for complex environment replication.
Experience and education
Minimum 4+ years in Python development, QA, or code review.
Assessment requirements
Two-Stage Assessment: Technical Task (60 minutes) – Coding assessment with recorded responses. Technical Interview (25 minutes) – Only for candidates who pass the task.
Eligibility
Work arrangement: Fully remote
Individual candidate engagement model with managed oversight.
Early submission with competitive rates preferred.
Available for immediate start upon project finalization.
Thriveth makes AI data-training work easier to find, understand, and navigate. We replace uncertainty with clear opportunities, realistic expectations, and insights from real application journeys.
Hiring companyMacgence
