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Member of Technical Staff, Machine Learning

Формат работы
remote (только Ireland)
Тип работы
fulltime
Грейд
middle
Английский
b2
Страна
Ireland
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
Member of Technical Staff, Machine Learning (Python/PyTorch/JAX): Building and improving production machine learning components across data, training, evaluation, and inference with an accent on reliable ML systems and real-world model behavior. Focus on fine-tuning models, maintaining data pipelines, debugging production issues, and optimizing latency, cost, reliability, and safety.

Location: Remote in Ireland

Company

Building proactive AI-native applications that help users manage conversations, errands, organization, and workflows with minimal prompting.

What you will do

  • Build and improve machine learning components across data, training, evaluation, and inference.
  • Fine-tune and adapt models within larger production systems.
  • Implement evaluation and testing to understand model behavior and improve accuracy, latency, reliability, and safety.
  • Build and maintain pipelines for real-world and synthetic data, training loops, and inference systems.
  • Debug model issues, performance problems, and production incidents, addressing root causes.
  • Collaborate with senior ML engineers, product, engineering, and research teams to ship iterative improvements.

Requirements

  • Must be based in Ireland for the remote role.
  • Strong foundations in machine learning and modern neural architectures.
  • Hands-on experience training, fine-tuning, or deploying machine learning models.
  • Production-quality Python development skills and willingness to learn new tools quickly.
  • Experience or familiarity with PyTorch, JAX, and production ML systems running on GPUs.
  • Ability to work through ambiguity, develop ownership, and improve systems through shipping and iteration.

Culture & Benefits

  • Work on real production systems from day one and learn how large-scale ML behaves outside research settings.
  • Collaborate in a small, high-talent-density, hands-on team.
  • Make decisions collectively while balancing rapid delivery with high-quality engineering.
  • Applications are reviewed by technical team members, with virtual and/or onsite interviews.
  • The process typically includes three, and no more than four, interviews followed by a prompt decision.

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