Cohere에서 LLM 추론 효율성을 극대화할 Staff Research Engineer를 채용합니다. 머신러닝 박사 학위와 LLM 아키텍처 및 추론 최적화에 대한 깊은 이해가 필수입니다. 모델 아키텍처 개선, GPU 가속, 추론 알고리즘 최적화를 통해 프로덕션 환경의 성능을 혁신하는 역할을 수행합니다. 글로벌 팀과 협업하며 최첨단 AI 모델의 가치를 높일 열정적인 인재를 찾습니다.
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!
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
Please Note: We have offices in Toronto, Montreal, San Francisco, New York, Paris, Seoul and London. We embrace a remote-friendly environment, and as part of this approach, we strategically distribute teams based on interests, expertise, and time zones to promote collaboration and flexibility. You'll find the Model Efficiency team concentrated in the EST and PST time zones, these are our preferred locations.
As a Staff Research Engineer, you will develop, prototype, and deploy techniques that materially improve how fast and efficiently our models run in production.
You may be a good fit for the model efficiency team if you:
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
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.
