cohere - Senior Member of Technical Staff, Safety and Security for Agents
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). • If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply! • We value and celebrate diversity and strive to create an inclusive work environment for all. We welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. • Full-Time Employees at Cohere enjoy these Perks: • 🤝 An open and inclusive culture and work environment • 🧑💻 Work closely with a team on the cutting edge of AI research • 🍽 Weekly lunch stipend, in-office lunches & snacks • 🦷 Full health and dental benefits, including a separate budget to take care of your mental health • 🐣 100% Parental Leave top-up for up to 6 months • 🎨 Personal enrichment benefits towards arts and culture, fitness and well-being, quality time, and workspace improvement • 🏙 Remote-flexible, offices in Toronto, New York, San Francisco, London and Paris, as well as a co-working stipend • ✈️ 6 weeks of vacation (30 working days!)
Responsibilities
• 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). • Working Location: • This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role. • Full-Time Employees at Cohere enjoy these Perks: • 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.
Benefits
• As a Member of Technical Staff in the Safety for Agents team, you will make a meaningful impact on the development of better, fairer, more trustworthy, and more secure 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. • Please Note: The existing team work in offices in London, Edinburgh, Paris, Toronto, and New York, but we also embrace being remote-friendly! For this role you need to have ~50% working day overlap with UK/EU timezone (e.g. US East is fine) but there are otherwise no restrictions on where you can be located for this role. • Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. • Education & learning stipend for conferences, courses, and coaching. • Remote-flexible, offices in Toronto, New York, San Francisco, London and Paris, as well as a co-working stipend • 6 weeks of paid vacation (30 working days!) • Budget for traveling to other offices if you are remote, plus an annual company offsite. • How and Where We Work: • Cohere is remote-friendly. We have offices in Toronto, San Francisco, New York City, London, Paris, Montreal, and more coming soon. • For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. • For those not near an office: a co-working benefit so you can work alongside others in your city. • Everyone receives a $500 home office stipend to set up your workspace properly.
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