almost 2 years ago
Responsibilities
- Design and develop multimodal AI systems integrating text, speech, and vision.
- Conduct research and experiments on advanced compute infrastructure.
- Explore novel approaches in multimodal representation learning, transfer learning, and related areas.
- Collaborate with research and engineering teams on multimodal AI systems.
- Tune and optimize large multimodal models and build evaluations to measure their performance.
- Identify and resolve issues in complex machine-learning codebases.
Requirements
- Exceptional software engineering skills and a proven track record of building robust, scalable systems.
- Strong command of Python and familiarity with JAX, PyTorch, and TensorFlow, including their multimodal capabilities.
- Knowledge of distributed training strategies for large-scale multimodal models.
- Familiarity with autoregressive models and multimodal applications such as image or video captioning and speech-to-text generation.
- Experience tuning and optimizing large multimodal models and building evaluations to measure their performance.
- Ability to work in complex ML codebases, identify issues, and resolve them.
- Publications in top-tier venues demonstrating multimodal AI research expertise are a bonus.
- Experience writing efficient GPU kernels with CUDA is a bonus.
Benefits
- Open and inclusive culture and work environment.
- Collaboration with a team at the cutting edge of AI research.
- Weekly lunch stipend, in-office lunches, and snacks.
- Full health and dental benefits, including a separate mental-health budget.
- 100% parental-leave top-up for up to six months.
- Personal enrichment benefits for arts and culture, fitness and well-being, quality time, and workspace improvement.
- Remote-flexible work, offices in Toronto, New York, San Francisco, London, and Paris, and a co-working stipend.
- Six weeks of vacation, equivalent to 30 working days.
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.