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Staff Research Engineer, Model Efficiency

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Cohere

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Job Description:

Large Language Models (LLMs) continue to push the boundaries of what AI systems can do — but inference is still the bottleneck. The Model Efficiency team is responsible for pushing the limits of LLM inference efficiency across our foundation models. We explore and ship breakthroughs across the model execution stack, including: model architecture and MoE routing optimization; decoding and inference-time algorithm improvements; software/hardware co-design for GPU acceleration; performance optimization without compromising model quality.

Job Responsibility:

develop, prototype, and deploy techniques that materially improve how fast and efficiently our models run in production

Requirements:

  • Have a PhD in Machine Learning or a related field
  • Understand LLM architecture, and how to optimize LLM inference given resource constraints
  • Have significant experience with one or more techniques that enhance model efficiency
  • Strong software engineering skills
  • An appetite to work in a fast-paced high-ambiguity start-up environment
  • Publications at top-tier conferences and venues (ICLR, ACL, NeurIPS)
  • Passion to mentor others
What we offer:
  • An open and inclusive culture and work environment
  • Work closely with a team on the cutting edge of AI research
  • Weekly lunch stipend, in-office lunches & snacks
  • Full health and dental benefits, including a separate budget to take care of your mental health
  • 100% Parental Leave top-up for up to 6 months
  • Personal enrichment benefits towards arts and culture, fitness and well-being, quality time, and workspace improvement
  • Remote-flexible, offices in Toronto, New York, San Francisco, London and Paris, as well as a co-working stipend
  • 6 weeks of vacation (30 working days!)

Additional Information:

Job Posted:
February 20, 2026

Employment Type:
Fulltime
Work Type:
Remote work
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