Requirements:

Strong statistical skills and experience evaluating scientific experiments related to data collection and model performance.
Extremely strong software engineering skills.
Strong expertise in designing and conducting data collection tasks, including working with human annotators.
Experience analyzing datasets with respect to their quality, biases, and suitability for training ML models.
Hands-on experience training large language models (LLMs) on distributed training infrastructures.
Familiarity with evaluating and improving the generalizability and robustness of ML systems.
Proficiency in programming languages such as Python and ML frameworks (e.g., PyTorch, TensorFlow, JAX).
Excellent communication skills to collaborate effectively with cross-functional teams and present findings.
One or more papers at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP).

Perks and Benefits:

A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.
Full health and dental benefits, including a separate budget for mental health.
RRSP matching, 401K, Pension Scheme.
100% Parental Leave top-up for up to 6 months, for either parent.
Annual enrichment benefits:
- Arts & culture, fitness/wellness, quality time, and a workspace improvement credit.
- Education & learning stipend for conferences, courses, and coaching.
6 weeks of paid vacation (30 working days!)
Budget for traveling to other offices if you are remote, plus an annual company offsite.

Make a meaningful impact on the development of better, fairer, more trustworthy, and more secure Large Language Models (LLMs).
Focus on data generation, post-training algorithms, and evaluation methods to ensure Safety in the next generation of models.
Work closely with other cross-functional machine learning teams and data annotation teams, and collaborate with product and policy teams.
Combine expertise in machine learning, ethical and responsible AI, experimental design, and data generation and management.
Tackle totally new scientific problems and test solutions with engineering skills.
Have autonomy and decision-making power to make LLMs better for society.