Senior Research Scientist, Model Evaluation (Remote‑Flexible)

Cohere

New York (NY)

On-site

USD 140,000 - 200,000

Full time

14 days+

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Benefits offered by this job

Open and inclusive culture
Work on cutting-edge AI research
Weekly lunches and snacks
Full health and dental benefits
Parental Leave top-up
Enrichment perks for arts and wellness
Remote-flexible, offices in multiple'
Co-working stipend
Six weeks of vacation

Job summary

Cohere is seeking a Senior Research Scientist, Model Evaluation in New York to lead the development of next-generation evaluation methods and infrastructure for measuring LLM progress. You will design ambitious benchmarks and work across teams to translate model feedback into robust, repeatable assessments.

The role emphasizes research, evaluation efficiency, and building scalable tooling to probe model performance, with a focus on aligning measurements with high-value capabilities.

Qualifications

  • Experience creating evaluation benchmarks for AI/ML models.
  • Experience translating model feedback into repeatable evaluations.
  • Research to advance state-of-the-art in LLM evaluation methods.
  • Strong software engineering skills.

Responsibilities

  • Create ambitious new evaluation benchmarks that push model capabilities.
  • Collaborate with cross-functional teams to translate feedback into trustworthy evaluations.
  • Conduct research to advance LLM evaluation methods and improve efficiency.
  • Build scalable, reusable tools for analyzing model performance.

Skills

Evaluation benchmarks
LLM training
LLM data synthesis
Evaluation tooling
Software engineering

Tools

Data synthesis pipelines
Scalable tooling

Job description

Cohere is seeking a Senior Research Scientist, Model Evaluation in New York to lead the development of next-generation evaluation methods and infrastructure for measuring LLM progress. You will design ambitious benchmarks and work across teams to translate model feedback into robust, repeatable assessments.

The role emphasizes research, evaluation efficiency, and building scalable tooling to probe model performance, with a focus on aligning measurements with high-value capabilities.

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