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Research Internship – Winter 2027

cohere

Nouveau Remote
Stage Remote Junior 🇬🇧 English
Python C++ Lua JAX PyTorch TensorFlow Transformers evaluation pipelines

Description du poste

About the role

Join Cohere as a Research Intern for the Winter 2027 term, collaborating with researchers to design and implement novel ideas and ship state‑of‑the‑art language models to production.

Key responsibilities

  • Conduct cutting‑edge machine learning research, building and training large language models.
  • Focus on projects that expand the frontier of knowledge in language modelling, evaluation, multimodal models, and optimisation.
  • Disseminate research results through publications, datasets, and open‑source code.
  • Contribute research outcomes that have practical applications in Cohere’s product development.

Required profile

  • Currently pursuing a PhD in Machine Learning, NLP, AI, or a related discipline (exceptional non‑PhD candidates considered).
  • Eligible for work authorization in Canada for the full internship period.
  • Experience with large‑scale distributed training, data annotation, and evaluation pipelines.
  • Familiarity with autoregressive sequence models such as Transformers.

Required skills

  • Python
  • C, C++
  • Lua
  • JAX
  • PyTorch
  • TensorFlow
  • Transformers
  • Large‑scale distributed training
  • Data annotation pipelines
  • Evaluation pipelines

What we offer

  • Weekly lunch stipend of $75 (or equivalent in local currency).
  • Full health and dental benefits, including a mental‑health budget.
  • RRSP matching, 401K, and pension scheme.
  • 100% parental‑leave top‑up for up to 6 months.
  • Annual enrichment benefits (arts, culture, fitness, workspace improvement).
  • Education & learning stipend for conferences, courses, and coaching.
  • 6 weeks of paid vacation (30 working days).
  • Travel budget for office visits and an annual company off‑site.
  • $500 home‑office stipend and co‑working benefit for remote employees.

Questions fréquentes

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Le contrat proposé est un Stage.
Source : ats:ashby

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