обновлено 4 дня назад
Member of Technical Staff, Machine Learning
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
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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