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12 дней назад

Senior Machine Learning Engineer (AI)

Формат работы
onsite
Тип работы
fulltime
Грейд
senior
Английский
b2
Страна
Denmark
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR
Senior Machine Learning Engineer (AI): Building machine learning systems that track players and the ball in sports footage, with an accent on model training, evaluation, and production inference across cloud and edge environments. Focus on designing efficient pipelines, improving understanding of model performance, and taking technically complex projects from concept to deployment.

Location: On-site at the Copenhagen office, Denmark

Company

hirify.global builds AI-powered sports cameras and analysis systems that capture matches and turn video into player, ball, and game-performance data.

What you will do

  • Train state-of-the-art machine learning models for sports video analysis.
  • Build efficient inference pipelines for cloud and edge environments.
  • Define and supervise data annotation tasks across machine learning projects.
  • Improve evaluation schemes to better understand model performance and limitations.
  • Plan and prioritize long-term technical work across projects.
  • Mentor colleagues through feedback, knowledge sharing, and collaboration.

Requirements

  • MSc, PhD, or equivalent practical experience in a relevant field.
  • Extensive hands-on experience delivering real-world, large-scale machine learning projects.
  • Strong coding and software engineering skills, including experience building maintainable production systems.
  • Experience owning technically complex projects from concept through deployment.
  • Sound technical judgment, rigorous analysis, and the ability to balance research, product, and engineering considerations.
  • Strong communication skills and the ability to influence technical direction across teams.

Culture & Benefits

  • Work with an AI team of more than 20 researchers and engineers across the full machine learning lifecycle.
  • Discuss current research and assess recent papers against product-specific challenges.
  • Take ownership, challenge the status quo, and drive measurable impact.
  • Continuous learning, technical feedback, and knowledge sharing are part of the working culture.
  • Collaborate across a diverse organization with a strong focus on customer value.

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