Machine Learning Engineer

Trackunit; Denmark; Remote; DKK 1,000,000 - 1,200,000; Full time

Job summary

Trackunit is seeking an ML Engineer to turn models into reliable systems for construction customers. You will operate at the intersection of data science and software engineering, shaping how ML gets built, deployed, and improved across Trackunit.

You’ll collaborate with data scientists, data engineers, and product teams to deploy reliable ML solutions, while balancing performance, cost, and reliability in a global, remote-friendly environment.

Qualifications

• MLOps experience taking machine learning solutions into production.
• Strong Python skills and software engineering practices, including testing, version control, code reviews, and CI/CD.
• Databricks expertise is a significant advantage.
• Hands-on experience building ML workflows in Databricks, tracking experiments, and managing models with MLflow, deploying and monitoring models in production.
• Understanding the ML lifecycle from feature prep to retraining.

Responsibilities

• Turn models into reliable, maintained systems used by construction customers.
• Collaborate with data scientists, data engineers, and product teams to deploy and monitor models.
• Shape deployment patterns from initial idea to production and continuous improvement.
• Balance reliability, performance, and cost in ML solutions.

Skills

MLOps

Python

CI/CD

Testing

Code reviews

Version control

Cloud platforms

Experiment tracking

Model deployment

ML lifecycle

Tools

Databricks

MLflow

Job description

Most machine learning never makes it to production. Your job is to make sure ours does. We are looking for an ML Engineer who turns models into reliable, maintained systems that construction companies actually use to make better decisions about their equipment and operations. You will work at the intersection of data science and software engineering, shaping how machine learning gets built, deployed, and improved at Trackunit.

What’s in it for you?

Bring machine learning into everyday use. Turn models and experiments into reliable products that help customers make better decisions about their equipment and operations.

Shape how we build and run ML. Influence the tools, engineering practices, and deployment patterns that take solutions from an initial idea through to production and continuous improvement.

Work on interesting data challenges. Explore how equipment, sensor, and contextual data can support useful predictions and insights for the construction industry.

Build alongside people with different strengths. Collaborate with data scientists, data engineers, and product teams, combining scientific thinking with practical software engineering and customer understanding.

Develop your craft and your career. Join an international environment with knowledge sharing, feedback, and support for your personal and professional development.

Who are you ideally?

• You have MLOps experience taking machine learning solutions into production and maintaining them beyond their initial release.
• You bring strong Python skills and solid software engineering practices, including testing, version control, code reviews, and CI/CD.
• Databricks expertise is a significant advantage.
• You ideally bring strong hands‑on experience building ML workflows in Databricks, tracking experiments and managing models with MLflow, and deploying and monitoring models in production.
• You understand the ML lifecycle, from feature preparation and reproducible training to evaluation, deployment, monitoring, and retraining.
• You can work with data scientists to assess model performance, recognise issues such as data leakage and drift, and translate experimental code into maintainable systems.
• You have experience with cloud platforms and tools for orchestrating ML workflows and serving predictions. You can make sensible trade-offs between reliability, performance, and cost.
• You enjoy shaping solutions with others, explaining technical choices clearly, and connecting engineering decisions to customer needs.
• Experience training, tuning, and validating machine learning or deep learning models would be a plus.
• Experience with time series, sensor or geospatial data, or distributed processing would be a plus.

You don’t need experience with every tool or data type listed. If you bring strong engineering skills and enjoy making machine learning useful in practice, we’d like to hear from you.

Our hiring process

Don't waste your time on writing the best possible cover letter for the job. We want you to create an impact that matters, and that's not in the cover letter.

• Virtual meet and greet. Meet with the Talent Acquisition Partner.
• A deeper conversation: We'll go into your experience and how you work, and you'll get a clearer picture of the team, the challenges, and what the role actually looks like day to day.
• Assignment-specific interview. We want you to get an insight into some of the concrete work tasks or projects related to the role. You will be given a case to prepare prior to the interview, and at the interview, you will present the case to relevant colleagues from across Trackunit, who you’ll also work closely with in the job.
• If needed, throughout the process we will obtain references from former employers and do background checks for level specific, if you have not provided these yourself.
• Offer presentation and walk-through. We’re lucky to have you!

Coming Together To Connect Construction

We’re committed to construction - one of the largest industries in the world. Over the past two decades, Trackunit has been pioneering technological progress within construction. Today, we are not only a leading IoT provider but a thought leader, supporting and shaping the agenda for an entire industry on a global scale.

We believe in taking a people approach in everything we do. Being human-centric is not restricted to our products – it’s a way of life at Trackunit. We’re proud to be a truly global team. Our colleagues get together in hubs spread across the globe, but we embrace the idea of working remotely and in environments that inspire you. Everything we do, we do it to eliminate downtime and build the most useful industry for the world.

The question is: Are you in?