Cohere에서 LLM 평가 방법론과 확장 가능한 평가 인프라를 구축할 시니어 연구 엔지니어를 채용합니다. LLM 성능 측정을 위한 데이터셋 및 환경 개발 경험이 필수이며, 최첨단 AI 모델의 성능을 검증하고 연구하는 핵심 역할을 수행합니다. 글로벌 팀과 협업하며 AI의 미래를 함께 만들어갈 역량 있는 분들의 많은 지원 바랍니다.
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 co-headquartered in Toronto and San Francisco, with key offices in London, New York City, Montreal, Seoul, Germany and Paris. Join us!
Evaluation is critical to making progress in scaling intelligence. As models continue to become superhuman in many real-world use cases, we must continue to develop new techniques to accurately measure our models' performance on frontier capabilities. In this role, you are responsible for creating next-generation evaluation methods and scalable infrastructure to measure LLM progress.
Develop evaluation benchmarks, datasets, and environments for measuring the bleeding edge of model capabilities
Conduct research to push the state-of-the-art in LLM evaluation methods, including training LLM judges; improving evaluation efficiency; and scalably building high-quality datasets
Build scalable tools for investigating and understanding evaluation results that are used by all members of technical staff at Cohere, as well as leadership and our CEO
Learn from and work with the best researchers and engineers in the field
You enjoy pushing the limits of what LLMs are capable of, and you have built high-quality evaluation resources to measure those capabilities (datasets, simulators, environments, etc.)
You have a track record of developing new methods and/or data to evaluate LLMs, e.g. publications at top-tier conferences, popular benchmarks, etc.
You have deep experience building with and around LLMs, and you have built tools for analyzing and understanding their performance
You have strong software engineering skills
A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.
Full health and dental benefits, including a separate budget for mental health.
RRSP matching, 401K, Pension Scheme.
100% Parental Leave top-up for up to 6 months, for either parent.
Annual enrichment benefits:
Arts & culture, fitness/wellness, quality time, and a workspace improvement credit.
Education & learning stipend for conferences, courses, and coaching.
6 weeks of paid vacation (30 working days!)
Budget for traveling to other offices if you are remote, plus an annual company offsite.
Cohere is remote-friendly. We have offices in Toronto, San Francisco, New York City, London, Paris, Montreal, and more coming soon.
For those in the office: a daily lunch program, plenty of snacks, and regular community and social events.
For those not near an office: a co-working benefit so you can work alongside others in your city.
Everyone receives a $500 home office stipend to set up your workspace properly.
If any of the above doesn’t line up exactly with your experience, we still encourage you to apply.
We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.
We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.
