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Member of Technical Staff (Post Training)

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Onsite

Member of Technical Staff (Post Training)

Cohere

Member of Technical Staff (Post Training)

Skills

PythonPyTorchMachine LearningKubernetes

2 months ago

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Important information


Contract type:

Permanent

Location:

London, UK

Work mode:

Onsite

Published on:

29 August 2026

What they need


  • Design and write high-performant and scalable software for training models
  • Consistently post-train the models to reach SOTA level performance
  • Coordinate with other specialist teams (Agentic, Code…) to produce models that have strong all encompassing performance
  • Craft and implement techniques to improve the performance and results of our training cycles both on the SFT and the RL regime
  • Research, implement, and experiment with ideas on our supercompute and data infrastructure
  • Learn from and work with the best researchers in the field

Benefits

  • Six weeks’ paid vacation
  • Equity / stock options
  • RRSP, 401(k), and Pension Scheme contributions
  • Coverage for 100% of your insurance premiums across health, dental, vision, and travel
  • Additional coverage for accessing mental health providers/services
  • Six months of fully paid parental leave, including adoption and surrogacy
  • Financial support for egg freezing and IVF in Canada and the UK
  • A monthly fitness and wellness allowance
  • Globally dispersed company that supports a remote work culture
  • A $2,000 annual education benefit for professional development
  • A weekly stipend for meals when working remotely and catered lunch when working from one of our global offices
  • A monthly arts and culture allowance
  • A monthly quality time allowance- Experience using large-scale distributed training strategies
  • Proficiency in Python and related ML frameworks such as JAX, Pytorch and XLA/MLIR
  • Bonus: paper at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP)
  • Experience with distributed training infrastructures (Kubernetes, Slurm) and associated frameworks (Ray)
  • Hands on experience on training large model at scale
  • Hands on experience with the post training phase of model training, with a strong emphasis on performance optimisation
  • Extremely strong software engineering skills

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