QA Engineer

pay

Required Skills
software quality assurance
test case design
test execution
defect reporting
data annotation
data labeling
ai model evaluation
documentation
english communication
cross-functional collaboration
About micro1
micro1 is a data lab that helps AI labs & Enterprises train foundational models and agents. We provide frontier evaluations and reinforcement learning environments to improve LLM capabilities, as well as contextual evaluations to monitor and improve AI agents in enterprise settings.

Our AI recruiting agent sources and vets domain experts for our data platform that enables rapid production of high-quality training data. Our research spans high-value, specialized domains (legal, medical, finance, coding, etc.), where expert-level data is the hardest to produce and most valuable for advanced model performance. Our pipeline performance system ensures both quality and velocity.

Role Title: QA Engineer

Role Type: Contractor

Location: Remote

micro1 is engaging QA Engineers to contribute to an AI training project with a forward-thinking customer. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters. This opportunity is ideal for those passionate about quality assurance and eager to be at the forefront of AI innovation, with a focus on data integrity, annotation, and comprehensive model evaluation.

Scope of Work

Design, execute, and document test cases and scenarios to validate AI outputs against defined requirements.
Review, annotate, and label data sets to enhance machine learning model training accuracy.
Evaluate, analyze, and report on model predictions, identifying inconsistencies, errors, or patterns for continuous improvement.
Collaborate with cross-functional teams to communicate findings, raise questions, and clarify project requirements.
Contribute to the development and refinement of testing methodologies specific to AI-driven systems.
Maintain detailed, organized records of evaluations, test results, and feedback through written documentation.
Provide actionable recommendations to optimize data quality and model performance.

Preferred Qualifications

Hands-on expertise in software quality assurance, with a strong understanding of test planning and defect reporting.
Prior experience with data labeling, annotation, reinforcement learning from human feedback (RLHF), or model evaluation.
Proficiency in English at B2 level or above for effective collaboration and reporting.
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