Member of Technical Staff

2 weeks ago


Toronto, Canada Cohere Full time

Member of Technical Staff - Public Sector Who are we? Our mission is to scale intelligence to serve humanity. We’re training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI. 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. We like to work hard and move fast to do what’s best for our customers. Cohere is a team of researchers, engineers, designers, and more, who are passionate about their craft. Each person is one of the best in the world at what they do. We believe that a diverse range of perspectives is a requirement for building great products. Join us on our mission and shape the future Why this role? Design and implement novel research ideas, ship state of the art models to production, and maintain deep connections to academia and the government. We have one of the highest ratio of compute to engineers in the world. We do not delineate strongly between engineering and research. Everyone will contribute to writing production code and conducting research depending on individual interest and organizational needs. We have all the compute, data, and talent available for you to do your best work. Responsibilities Design, build and scale agentic AI systems for serving mission critical use cases Research, implement, and experiment with ideas on our supercompute and data infrastructure Learn from and work with the best researchers in the field Execute across the full AI stack and ship products to serve public interest Qualifications Canadian citizenship and eligibility for security clearance (required for this role) Extremely strong software engineering skills Proficiency in Python and related ML frameworks Experience training, evaluating, and using large language models (LLMs) as an end-user Experience using large-scale distributed (GPU) LLM training strategies Bonus: paper at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP) If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply Benefits



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