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CohereCohereUnited Kingdom

Member of Technical Staff, Modeling

Build and scale production AI systems while researching and experimenting with novel modeling ideas. The role requires strong software engineering, Python and ML framework proficiency, GPU kernel development, distributed training experience, and familiarity with Transformer-based sequence models.

Salary not listed
RemoteML Engineering

About the role

Responsibilities

  • Design, build, and scale AI systems for serving users.
  • Research, implement, and experiment with ideas on supercompute and data infrastructure.
  • Write production code and conduct research based on individual interests and organizational needs.
  • Learn from and collaborate with leading researchers.

Requirements

  • Extremely strong software engineering skills.
  • Proficiency in Python and machine-learning frameworks and tools including TensorFlow, TF-Serving, JAX, and XLA/MLIR.
  • Experience writing GPU kernels using CUDA.
  • Experience with large-scale distributed training strategies.
  • Familiarity with autoregressive sequence models such as Transformers.

Nice to Have

  • Papers published at top-tier venues such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, or EMNLP.

Compensation and Benefits

  • Weekly lunch stipend of $75/£75 or equivalent in local currency.
  • Full health and dental benefits, including a separate mental-health budget.
  • RRSP matching, 401(k), or pension scheme.
  • 100% parental-leave top-up for up to six months for either parent.
  • Annual enrichment benefits covering arts and culture, fitness and wellness, quality time, and workspace improvements.
  • Education and learning stipend for conferences, courses, and coaching.
  • Six weeks of paid vacation.
  • Travel budget for remote employees visiting other offices and an annual company offsite.
  • Co-working benefit for employees not near an office.
  • $500 home-office stipend.

Skills

PythonTensorFlowtf-servingJAXxlamlirCUDAgpu kernelsDistributed TrainingTransformersautoregressive modelssupercomputingMachine Learning

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