Machine Learning Engineer

Jabra; Ballerup; On-site; DKK 598,000 - 897,000; Full time

Benefits offered by this job

Flexible/Hybrid work options

Work with cutting-edge ML technologies

Collaborative engineering culture

Job summary

GN is seeking a Machine Learning Engineer to build models on real-world product data and bring them into production. You will join the Engineering Excellence team and work across the model lifecycle, from data exploration to deployment and ongoing improvement.

You will collaborate with ML architecture, software engineering and data science colleagues in a production-oriented environment, using Python, Azure ML, Databricks and Azure DevOps to deliver reliable product capabilities.

Qualifications

• Experience in applied machine learning from experimentation to deployment.
• Practical ML on real-world data with Python.
• Ability to design and run useful experiments and interpret results.
• Curious about emerging ML technologies and model behavior.
• Ability to explain model capabilities and limitations to stakeholders.
• Ability to work independently within an established technical direction.
• Experience fine-tuning language models on domain-specific data is a plus.

Responsibilities

• Explore real-world product data, identify useful features and assess data-supported problems.
• Build and evaluate classical ML and deep learning models, testing new approaches for product value.
• Take models from experimentation through deployment, contributing to data pipelines.
• Assess model performance on production data and improve reliability.
• Work in Python with Azure ML, Databricks and Azure DevOps, using scikit-learn, TensorFlow or PyTorch.
• Use AI-assisted development tools to support experimentation and engineering.
• Collaborate to turn experimental results into maintainable product capabilities.

Skills

Applied machine learning

Python

Azure ML

Databricks

Azure DevOps

scikit-learn

TensorFlow

PyTorch

Tools

Azure ML/Databricks

Azure DevOps

Python tooling

Job description

Build models on real-world product data and bring them into production

In the role of Machine Learning Engineer in the Engineering Excellence team within Software Solutions. You will work on applied machine learning for GN's products, contributing across the model lifecycle, from exploring data and evaluating approaches to deployment and ongoing improvement. You will explore advanced machine learning use cases and turn promising ideas into reliable capabilities for GN’s products.

This is an opportunity to work at the forefront of applied ML, where you will help investigate new possibilities, challenge established approaches, and contribute to creating the foundations for how the team works and delivers.

The Team you will be part of:

Based in Ballerup near Copenhagen, Engineering Excellence works with engineering teams across GN to build quality software capabilities for our products.

You will join a relatively new machine learning team, collaborating with colleagues who bring expertise in ML architecture, software engineering and data science. Together, we explore use cases using real-world product data, where understanding data quality, limitations, and useful signals is essential to building reliable models and product capabilities.

Your contribution is appreciated, and you will:

• Explore real-world product data, identify useful features and assess which advanced problems the available data can support.
• Build and evaluate classical machine learning and deep learning models, testing new approaches where they can create product value.
• Take models from experimentation through deployment, contributing to the data infrastructure and pipelines they need.
• Assess model performance on real-world data and improve reliability in production.
• Work in Python with Azure ML, Databricks and Azure DevOps, using tools such as scikit-learn, TensorFlow or PyTorch.
• Use AI-assisted development tools to support experimentation and engineering, applying sound judgement to their outputs.
• Collaborate with colleagues to turn experimental results into maintainable product capabilities and help shape effective ways of working as the team grows.

To perform well in the role, we imagine that you:

• Bring relevant experience in applied machine learning, including taking models from experimentation through deployment and improving them in production.
• Have practical experience with classical machine learning and deep learning on real-world data, and write maintainable Python.
• Take initiative in exploring open-ended problems, designing useful experiments and moving work forward independently within an established technical direction.
• Can design useful experiments, evaluate results critically and explain what a model can and cannot do.
• Are curious about emerging ML technologies, data, model behaviour and product needs, and can evaluate results critically to explain what a model can and cannot do.
• Focus on delivering reliable outcomes, seek input when helpful, and constructively challenge assumptions and approaches to improve the solution.
• Experience fine-tuning language models on domain-specific data is an add‑on, as is experience working with regulated products.

At GN we pride ourselves on encouraging flexible work whenever possible. We trust our people to fulfill their responsibilities, to know when in‑person collaboration is better than hybrid, and to be present when it's needed most.

We are focused on an inclusive recruitment process

All applicants will receive equal consideration for employment. As such, we encourage you to submit your CV without a photo to ensure an equal and fair application process.

Should you have any special requirements for the interview, please let the Hiring Manager know upon accepting the invitation to the interview.

Join us in bringing people closer

GN brings people closer through our advanced intelligent hearing, audio, video, and gaming solutions. Inspired by people and motivated by innovation, we deliver technology that enhances the senses of hearing and sight. We enable people with hearing loss to overcome real-life problems, improve communication and collaboration for businesses, and provide great experiences for audio and gaming users.

We hope you will join us on this journey and look forward to receiving your application.