Tech-first. Ego-free. User-obsessed.

Over one million Danes use Skatteguiden to understand and optimise their taxes. We automate tax tracking, surface deductions people didn't know they had, and give users the insight they need to make better financial decisions — all connected directly to the Danish tax authority. We're an independent company of around 50 people based in Copenhagen, with a 4.7-star rating and a simple mission: make financial insight accessible to everyone, not just those who can afford an accountant.

We have an ambitious multi-year vision for where we take the product next. This role owns the ML systems that make it possible.

About the role

Skatteguiden sits on a rich proprietary dataset — years of tax and financial data across more than a million Danes. That data fuels the intelligence behind our product, and the ML systems built on top of it are what turn it into something users feel.

You'll architect and orchestrate those ML systems — how we run experiments, compare models, deploy them, and manage them cleanly in production. The systems themselves serve user-facing insights: think surfacing opportunities users would otherwise miss, and helping them understand the consequences of financial decisions before they make them.

We have models in production today, but the deployment, serving, and lifecycle architecture deserves a dedicated owner who's done this before.

You'll join the AI Products team as the senior ML profile, working closely with our existing Data Scientist, our AI Engineer, and our incoming Applied AI Engineer. The team is small and dynamic, and this role carries meaningful technical leadership for the ML systems across Skatteguiden.

What you'll do

- Architect and orchestrate our ML layer. Design how we run experiments, compare models, deploy them, and keep them healthy in production. Models, features, pipelines, and the platform that connects them.

- Be the data source expert for the AI team. Partner with the data and backend teams, get the right data in the right shape for ML, and make that pipeline reliable.

- Build systems that compound. Personalisation and pattern detection that become more useful as more users are onboarded.

- Own your systems in production. Training, evaluation, rollout, monitoring, retraining. End-to-end.

- Set the technical bar. As the senior ML profile, you help shape how the team thinks about modelling, evaluation, and production ML discipline.

Who we're looking for

You're a senior ML engineer who has shipped production ML at scale and wants to build something with genuine user impact. You care about models, but you care more about whether they ship, whether they work, and whether they actually change what a user sees.

If you're excited about the task of lifting MLOps to the next level — enabling both rapid experimentation and reliable deployment of our models and agentic systems — we'd like to hear from you.

We also want to be upfront: priorities will shift, and the first months will include some classic data engineering work alongside the ML systems work. If LLMs are the only thing you want to do, this isn't the role.

Must-have

- Senior hands-on experience shipping production ML systems. You've owned models end-to-end: features, training, evaluation, deployment, monitoring, iteration. We want someone who can sit across from us and say "I've seen this, this is how it scales."

- Strong MLOps. Backend serving patterns, model-serving optimisation, deployment architecture, lifecycle management. This is the gap we're filling.

- Experience or strong interest in agentic systems and LLM-specific deployment concerns.

- Data engineering chops. Comfortable designing data pipelines and infrastructure, and partnering with the data team.

- Strong skills in Python and modern ML tooling.

- Clear communication, particularly on architectural trade-offs. You can explain to engineers, product people, and leadership why a system is designed the way it is.

- Security, privacy, and compliance awareness. You work with GDPR-sensitive data thoughtfully and you understand what ISO 27001 implies for model development and deployment.

- Genuine enthusiasm for AI. You use AI tools daily in your own work and you're always looking for new ways to apply them.

Nice-to-have

- Experience with financial, tax, or PSD2 banking data.

- Experience evaluating the LLM vs. classical ML boundary in production.

- Familiarity with Azure.

- Danish language — useful but not required. The team works in English.

Why join us

- A moat to build. Our ML layer is the technical foundation of Skatteguiden's long-term advantage. You'll be architecting and owning it.

- Real influence. Small team, high autonomy, and the decisions you make will shape our AI direction for years.

- The product matters. Over one million Danes use Skatteguiden. Your work turns their collective data into something each one of them benefits from.

- A serious roadmap. Concrete features, a multi-year vision, and an executive team that backs it.

- Solid company. Independent, profitable, growing. No VC pressure, no bloated roadmap. Just a clear mission and a team that cares.

- A company that's serious about AI. We use AI across the business, and we're only going deeper. Being a small, focused team means we can move fast on this — no lengthy approval chains, no slow rollouts.

- Good people. We take the work seriously without taking ourselves too seriously. The culture is professional, informal, and genuinely collaborative.

Practical details

Location: Copenhagen. We're an office-first company, and it's a deliberate choice. We're moving fast, we have big ambitions, and we genuinely believe that being physically together is one of the things that makes that possible. Things move quicker when you can turn your chair and talk to someone.

We're a five-minute walk from Rådhuspladsen, stocked with everything from fruit to fizzy drinks to afternoon snacks, and lunch is at Claus Meyer's canteen just across the street — proper food, not an afterthought. If you're putting in a long day, dinner is covered too.

Start: As soon as possible.

Salary: Competitive and based on your experience.

Our lean hiring process

1. Screening call with our recruiter (30 min) ONLINE

2. Technical interview with the Head of AI Products and an AI engineer (120 min) ONSITE

3. Case / technical deep-dive with the AI Products team (60 min) ONSITE

4. Final conversation with the CTO and CEO (30 min) ONSITE