Member of Technical Staff, North Modelling (Evals)

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. 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.

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.

Requirements

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.

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; 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