Senior Machine Learning Engineer

What is working at Dtyped like?

At Dtyped, it’s all about data and people. You’ll join a small consulting team that helps clients get the most out of their data. Sometimes you’ll work alongside colleagues, sometimes as part of the client’s team.

We’re creating a group where hard work, fun, and good food go together naturally. Every week we meet at the office. Can’t make it? No problem, freedom and flexibility come first. Families are part of the picture too, at team events they’re welcome to join in.

We’re a young startup with no heavy rules or boxes to fit into. Your ideas really matter here. Are you curious, responsible and ready to grow with people who look out for each other? Then you’ll feel right at home with us.

Who are you?

You’re an experienced Machine Learning Engineer who knows how to design and scale data-driven systems — ideally in the cloud. Familiar with Databricks? That’s a plus, but not required. What matters most is that you’re confident working with at least one of the major cloud providers (Azure, AWS, or GCP) and that you bring a cloud-first mindset to building and deploying ML solutions.

We’re looking for someone who is as engaged as we are: helping clients for real, thinking beyond the tooling and joining the conversation about their bigger data ambitions. Got Databricks or cloud certifications? That’s definitely a plus with us.

Since we’re still a small team, we’d love someone with a touch of entrepreneurship. Not just building, but also sharing ideas on how we can grow as a team and as a company.

Requirements

• You hold a Bachelor’s (HBO) or Master’s degree, preferably IT or business related;
• You have 3+ years of experience engineering machine learning solutions in one of the 3 public clouds (preferably with Databricks);
• Strong proficiency in Python (PySpark, MLFlow, scikit-learn or similar libraries);
• Hands-on experience with MLOps practices: model training, versioning, deployment and monitoring;
• You know your way around CI/CD systems like GitHub Actions or Azure DevOps Pipelines.

Bonus points

• Experience with real-time or streaming ML pipelines using Structured Streaming on Databricks;
• Knowledge of Unity Catalog for governance and securing ML assets;
• Familiarity with Databricks Feature Store (or a 3rd party feature store) for managing and reusing features;

👉 Missing one or two boxes? No worries. We’d love to have a chat anyway. For us, it’s just as much about you as a person, your drive and how you click with the team.