Senior Member of Technical Staff (Safety and Security for Agents)

• As a Member of Technical Staff in the Safety for Agents team, you will make a meaningful impact on the development of better and fairer Large Language Models (LLMs)
• Your primary focus will be on data generation, post-training algorithms, and evaluation methods to ensure Safety in the next generation of models that can access external resources and take actions in the world
• You will work closely with other cross-functional machine learning teams and data annotation teams, and will also collaborate with product and policy teams
• This role combines expertise in machine learning, ethical and responsible AI, experimental design, and data generation and management
• It will require curiosity to tackle totally new scientific problems, engineering skills to implement the pieces we need to test solutions to these, and a desire to dive into messy data and results
• You will be on a small team with a lot of autonomy and decision-making power, responsible for making the next generation of LLMs better for society as a whole

Benefits

• Six weeks’ paid vacation
• Equity / stock options
• RRSP, 401(k), and Pension Scheme contributions
• Coverage for 100% of your insurance premiums across health, dental, vision, and travel
• Additional coverage for accessing mental health providers/services
• Six months of fully paid parental leave, including adoption and surrogacy
• Financial support for egg freezing and IVF in Canada and the UK
• A monthly fitness and wellness allowance
• Globally dispersed company that supports a remote work culture
• A $2,000 annual education benefit for professional development
• A weekly stipend for meals when working remotely and catered lunch when working from one of our global offices
• A monthly arts and culture allowance
• A monthly quality time allowance

• If any of the above doesn’t line up exactly with your experience, we still encourage you to apply
• Extremely strong software engineering skills
• Experience analyzing datasets with respect to their quality, biases, and suitability for training ML models
• Proficiency in programming languages such as Python and ML frameworks (e.g., PyTorch, TensorFlow, JAX)
• One or more papers at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP)
• Strong statistical skills and experience evaluating scientific experiments related to data collection and model performance
• Strong expertise in designing and conducting data collection tasks, including working with human annotators
• Excellent communication skills to collaborate effectively with cross-functional teams and present findings
• Familiarity with evaluating and improving the generalizability and robustness of ML systems
• Hands-on experience training large language models (LLMs) on distributed training infrastructures