Member of Technical Staff, North Modelling (Evals)

Who are we? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role overview: North is Cohere’s AI workspace platform for enterprises: a secure, customizable environment where companies can use AI across their real workflows while maintaining control over sensitive data. North connects AI agents with workplace tools, applications, and business context, helping users delegate complex work, build automations, inspect outputs, and collaborate with AI in production environments. As North becomes more capable, one of the most important questions is also one of the hardest: how do we know whether the model is actually getting better for the workflows customers care about? This role is about being the voice of North inside modelling. You will build the evaluation systems, feedback loops, and applied modelling workflows that make sure model progress translates into better product outcomes for North users. You will work closely with North product teams, customer-facing teams, and modelling teams to define what “good” means across the product surface, turn real usage and product direction into high-quality evals, and use those evals to guide model selection, patches, and regular model updates. This is neither a pure research role nor a conventional product engineering role. It is a rigorous applied MLE role for someone who cares deeply about measurement, model behavior, and real-world product quality. You should be excited by the craft of building careful evals: evals that capture messy agentic workflows, reflect actual customer needs, resist superficial benchmark hacking, and provide useful signal for where the product and models need to go next. Please note: this team works closely across Europe and East Coast North America time zones. We are open to candidates who can collaborate effectively within those hours. Key responsibilities: - Own the eval strategy for North: define what we need to measure across agent workflows, tool use, enterprise knowledge work such as deep research, document creation or editing, and other human-AI interactions. - Build high-quality evals from the realities of the product: user feedback, production failures, privacy-preserving usage logs, internal dogfooding, customer needs, and forward-looking product goals. - Create systems that continuously turn what North is learning from users, customers, and feature teams into evals, so measurement keeps pace with the product rather than becoming a static benchmark. - Be the voice of North inside modelling: extract clear insight from eval results, customer-derived data and product context, then turn model failures, capability gaps, and North-specific needs into actionable recommendations for the central modelling teams. Qualifications: - You have improved LLM-powered, agent-powered, or AI-product systems through evals, feedback loops, data curation, prompting, model adaptation, or model selection. - You care deeply about evaluation as a craft: representative tasks, precise rubrics, clean data, failure analysis, regression tracking, and knowing when a metric is giving false confidence. - You have strong applied MLE judgment and can reason clearly about model behavior, eval validity, product outcomes, and production tradeoffs. - You are comfortable working close to users and product teams, while translating messy qualitative signals into measurement that other modelling teams can act on. - You are self-directed, practical, and motivated by open-ended problems where the right answer requires both technical depth and product understanding. - You care about making AI systems genuinely useful in real enterprise settings, not just better on abstract benchmarks. If some of the above does not line up perfectly with your experience, we still encourage you to apply. 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 Empl

You have improved LLM-powered, agent-powered, or AI-product systems through evals, feedback loops, data curation, prompting, model adaptation, or model selection. You care deeply about evaluation as a craft: representative tasks, precise rubrics, clean data, failure analysis, regression tracking, and knowing when a metric is giving false confidence. You have strong applied MLE judgment and can reason clearly about model behavior, eval validity, product outcomes, and production tradeoffs. You are comfortable working close to users and product teams, while translating messy qualitative signals into measurement that other modelling teams can act on. You are self-directed, practical, and motivated by open-ended problems where the right answer requires both technical depth and product understanding. You care about making AI systems genuinely useful in real enterprise settings, not just better on abstract benchmarks. If some of the above does not line up perfectly with your experience, we still encourage you to apply.