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10 часов назад

Data Quality & Benchmarking Engineer (AI)

Тип работы
fulltime
Грейд
middle
Английский
b2
Страна
Germany
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

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TL;DR

Data Quality & Benchmarking Engineer (AI/Python): Strengthening the quality process of the DataPilot product and ML platform components with an accent on early validation, dataset-based regression testing, and benchmarking. Focus on designing validation scenarios, measuring system performance, and reducing the gap between technical design and final implementation.

Location: Darmstadt, Germany

Company

hirify.global is a company developing DataPilot, an AI-driven data and ML platform.

What you will do

  • Review product requirements and technical designs to translate them into validation scenarios and test plans.
  • Define and maintain a structured catalog of test datasets, including golden, dirty, and regression datasets.
  • Validate data processing behavior, labeling flows, correction flows, and user-facing results.
  • Design and execute benchmark scenarios for timeseries platform features and Kubeflow pipelines.
  • Measure and analyze runtime, memory usage, throughput, scalability limits, and failure behavior.
  • Collaborate with cross-functional engineering teams to improve the Definition of Done and quality gates.

Requirements

  • 2+ years of experience in quality engineering, SDET, or data platform testing.
  • Experience testing complex backend services, APIs, and data processing pipelines.
  • Practical experience with Python-based testing or scripting.
  • Ability to convert product requirements (PRDs) into concrete test scenarios.
  • Familiarity with CI/CD workflows and containerized environments (Docker).
  • Must be based in or able to work in Darmstadt, Germany.

Nice to have

  • Basic understanding of Data/ML Pipelines.
  • Experience with performance benchmarking.
  • Experience with time-series data processing.

Culture & Benefits

  • Embedded role within the development team to ensure early validation and feedback.
  • Focus on quality as a shared engineering practice rather than a final handover step.
  • Opportunity to work on high-impact AI and ML platform components.
  • Permanent employment contract.

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