28 days ago
Responsibilities
- Design and write high-performing, scalable software for model training.
- Develop tools that support and accelerate research and LLM training.
- Coordinate with infrastructure, efficiency, serving, and scientific engineering teams.
- Research, implement, and experiment with ideas on cluster and data infrastructure.
- Collaborate with scientists, engineers, and cross-functional teams to build production-ready solutions.
Requirements
- Extremely strong software engineering skills.
- Proficiency in Python and related ML frameworks such as JAX, PyTorch, and/or XLA/MLIR.
- Experience using and debugging large-scale distributed training strategies, including memory and speed profiling.
- Experience with distributed training infrastructure such as Kubernetes and associated frameworks such as Ray.
- Experience in ML and LLM academic research.
- Familiarity with test-driven development methods, clean code, and reducing technical debt.
Benefits
- Weekly lunch stipend of $75/£75 or equivalent in local currency.
- Full health and dental benefits with a separate mental health budget.
- RRSP matching, 401K, and pension scheme.
- 100% parental leave top-up for up to six months for either parent.
- Annual arts and culture, fitness and wellness, quality time, workspace improvement, education, learning, conference, course, and coaching benefits.
- Six weeks of paid vacation, or 30 working days.
- Travel budget for remote employees visiting other offices and an annual company offsite.
- Remote-friendly work arrangement with offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin, and Seoul.
- Daily lunch program, snacks, and community and social events for office employees.
- Co-working benefit for employees not near an office.
- $500 home office stipend.
Tech Stack
Categories
AI ResearchML Engineering
About Cohere
Cohere builds large language models and an enterprise AI platform that companies use for search, summarization, and workflow automation, delivered via API or private deployments. Founded in 2019 and headquartered in Toronto, it focuses on multilingual models, data controls, and options to run across major clouds or on-premises. The business is privately held and serves security- and compliance-sensitive organizations.