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3 дня назад

Research Engineer (QC Automation)

150 000 - 250 000$
Формат работы
remote (Global)/onsite
Тип работы
fulltime
Грейд
middle
Английский
b2
Страна
Singapore/US/Europe
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

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TL;DR
Research Engineer (QC Automation) (AI training data): Building scalable quality-control and validation systems for reinforcement-learning environments and post-training AI datasets with an accent on human-judgment-based evaluation, data quality standards, and agent-output grading. Focus on designing experiments and metrics, diagnosing agent failure modes, and building auditing, sampling, and rule-based or model-assisted validation pipelines.

Location: On-site in San Francisco, California for U.S.-based candidates; on-site in Singapore for Southeast Asia-based candidates. Fully remote independent contractor arrangements are considered for candidates based elsewhere, particularly in Europe.

Salary: $150,000–$250,000 USD annually

Company

hirify.global is an early-stage company building infrastructure for reinforcement-learning environments and post-training AI datasets.

What you will do

  • Automate quality control for training data produced through the platform infrastructure.
  • Define and enforce end-to-end quality standards for AI training data.
  • Design experiments, benchmarks, rubrics, and metrics to evaluate agent outputs across diverse tasks.
  • Build auditing systems with sampling strategies and rule-based or model-assisted validation pipelines.
  • Partner with data vendors to diagnose quality issues and agent failure modes, then improve data-generation processes.
  • Integrate quality-control insights into infrastructure tools and the vendor portal to reduce anomalies and edge cases.

Requirements

  • 2–4 years of experience in engineering or research roles, ideally in quality-control automation or data quality.
  • Proficiency in Python, Docker, and Linux environments.
  • Experience building scalable data-validation pipelines and automated QA/QC systems end-to-end without a prescribed roadmap.
  • Experience with benchmarks and evaluations for reinforcement-learning training data, including realistic tasks, reliable rubrics, and useful trajectories.
  • Strong knowledge of statistics and experience designing metrics and QA/QC processes.
  • Strong written and verbal communication skills for cross-timezone collaboration, plus the ability to work autonomously in a fast-paced environment.

Culture & Benefits

  • Work on a critical quality function within an approximately 15-person engineering team.
  • Collaboration with data vendors and infrastructure teams.
  • Early-stage environment emphasizing curiosity, intellectual range, and autonomy.
  • Visa sponsorship is available for qualifying candidates.

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