1 year ago
Responsibilities
- Stay current with research in code LLMs, agents, and related fields and implement novel ideas in production systems.
- Design and implement scalable strategies for training code models and deploy agent frameworks for inference and sampling.
- Collaborate with the pretraining team, create supervised fine-tuning trajectories, and work on existing and new reinforcement learning algorithms.
- Improve existing benchmarks and design new benchmarks reflecting enterprise user needs.
- Lead experiments on state-of-the-art compute infrastructure using frontier LLMs.
Requirements
- PhD in Computer Science, Machine Learning, or a related field.
- Publications in top-tier venues such as NeurIPS, ICML, ICLR, ACL, or EMNLP.
- Deep expertise in code LLMs and agent systems, including actively contributing to code model development.
- Hands-on experience with frontier LLMs and applications in code generation or automation.
- Strong software engineering skills with proficiency in Python and PyTorch, TensorFlow, or similar frameworks.
- Experience with distributed systems, cloud infrastructure, and scalable architectures.
- Proactive, self-motivated approach and passion for solving ambitious, open-ended problems.
Benefits
- Remote-friendly, flexible work environment; team collaboration primarily spans ET to CET time zones.
- Weekly lunch stipend, in-office lunches, and snacks.
- Full health and dental benefits plus a separate mental health budget.
- 100% parental leave top-up for up to 6 months.
- Personal enrichment benefits for arts and culture, fitness and well-being, quality time, and workspace improvement.
- Remote-flexible work with offices in Toronto, New York, San Francisco, London, and Paris, plus a co-working stipend.
- Six weeks of vacation, or 30 working days.
- Equity and competitive compensation are offered, with no specific base salary stated.
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.