Startups · AI

Member of Technical Staff, Model Efficiency

Cohere · New York · Remote

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About Cohere

Backed by AI Grant.

About the role

Our team is a fast-growing group of researchers and engineers focused on building reliable ML systems and pushing the boundaries of LLM inference efficiency. We develop techniques that improve how models execute in production, driving lower latency, higher throughput, and consistent quality across diverse workloads.

What they're looking for

  • 5+ years of experience writing high-performance, production-quality code
  • Strong programming skills in C++ or Python (Rust/Go also welcome)
  • Experience working with large language models and familiarity with the LLM inference ecosystem (e.g., vLLM, SGLang, etc.)
  • Ability to diagnose and resolve performance bottlenecks across the model execution stack
  • A strong bias for action — you ship fast, measure impact, and iterate
  • GPU programming, CUDA, or low-level systems optimization
More about this role

Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.

We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.

We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft.

We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us!

Our team is a fast-growing group of researchers and engineers focused on building reliable ML systems and pushing the boundaries of LLM inference efficiency. We develop techniques that improve how models execute in production, driving lower latency, higher throughput, and consistent quality across diverse workloads.

Please Note: We have offices in...

Read the full posting on Cohere's site ↗

Modeling

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