Member of Technical Staff, North Modelling (Evals) in London

Who are we? The company 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. The company 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!

Why This Role? North is the company’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 behaviour, 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.

As a Member of Technical Staff, North Modelling (Evals), You Will:

• 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.

You May Be a Good Fit If:

• 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 behaviour, 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.

Location: 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.

Full-Time Employees at the company 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.
• 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.

How and Where We Work: The company is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening 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.

We 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, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.