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Job
Staff Research Engineer, Model Efficiency
Backend Developer
• Remote
• Full-time
• 📍 New York
As a Staff Research Engineer on Cohere's Model Efficiency team, you'll develop and ship breakthroughs that improve LLM inference efficiency across the foundation model stack, from model architecture to GPU acceleration.
Stack
Responsibilities
- ▹Optimize model architecture and MoE routing
- ▹Improve decoding and inference-time algorithms
- ▹Co-design software and hardware for GPU acceleration
- ▹Optimize performance without compromising model quality
- ▹Develop, prototype, and deploy techniques that materially improve how fast and efficiently models run in production
Requirements
- ▹PhD in Machine Learning or a related field
- ▹Understanding of LLM architecture and how to optimize LLM inference under resource constraints
- ▹Significant experience with one or more techniques that enhance model efficiency
- ▹Strong software engineering skills
- ▹An appetite for a fast-paced, high-ambiguity startup environment
Nice to have
- ▹Publications at top-tier conferences and venues (ICLR, ACL, NeurIPS)
- ▹A passion to mentor others
Soft skills
MentorshipComfort with high ambiguity
What we offer
- ▹Weekly lunch stipend of about $75/£75 (or local equivalent)
- ▹Full health and dental benefits, with a separate mental health budget
- ▹RRSP matching, 401K, or pension scheme
- ▹100% parental leave top-up for up to 6 months, for either parent
- ▹Annual enrichment benefits: arts & culture, fitness/wellness, quality time, workspace improvement
- ▹Education & learning stipend for conferences, courses, and coaching
- ▹6 weeks (30 working days) of paid vacation
- ▹Budget for traveling to other offices, plus an annual company offsite
- ▹$500 home office stipend; co-working benefit for remote employees
About the company
Cohere is the leading security-first enterprise AI company, building cutting-edge foundation models and end-to-end products that solve real business problems. Co-headquartered in Toronto and San Francisco, with key offices in London, New York City, Montreal, Seoul, Germany and Paris.
Education: PhD gépi tanulásban vagy kapcsolódó területen
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