6 months ago
Responsibilities
- Develop data generation, post-training algorithms, and evaluation methods for safety in the next generation of large language models.
- Design and conduct experiments addressing new scientific problems in safety for agents.
- Implement engineering solutions needed to test machine learning and safety approaches.
- Analyze messy data and experimental results to improve model quality, fairness, trustworthiness, and security.
- Collaborate with machine learning, data annotation, product, and policy teams.
Requirements
- Strong statistical skills and experience evaluating scientific experiments involving data collection and model performance.
- Extremely strong software engineering skills.
- Strong expertise designing and conducting data collection tasks with human annotators.
- Experience analyzing datasets for quality, bias, and suitability for training machine learning models.
- Hands-on experience training large language models on distributed training infrastructures.
- Familiarity with evaluating and improving the generalizability and robustness of machine learning systems.
- Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, and JAX.
- Excellent communication skills for cross-functional collaboration and presenting findings.
- One or more papers at top-tier venues such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, or EMNLP.
Benefits
- Open and inclusive culture and work environment
- 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 with offices in Toronto, New York, San Francisco, London, and Paris
- Co-working stipend
- Six weeks of vacation, or 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.