Machine Learning Engineer

About TOMRA

TOMRA builds sensor-based sorting technologies that support the recycling and waste management sector worldwide. Its work focuses on innovation and sustainability, with more than 9,000 sorting systems installed across over 100 countries. The company emphasizes teamwork, responsibility, continuous improvement, and an inclusive environment where different viewpoints are welcomed.

Role Overview

As a Machine Learning Engineer, you will create, deploy, and support data- and model-focused systems that deliver production-ready machine learning capabilities. The role combines ML engineering, data platform work, service development, and software engineering, with strong ownership over assigned work and close coordination with internal stakeholders.

Key Responsibilities

• Develop complete ML training workflows covering ingestion, validation, transformation, training, and evaluation.
• Package, optimize, and deploy models for inference use cases.
• Work with the platform team to run models in the cloud.
• Collaborate with the AI runtime team to run models on edge devices and sorting systems.
• Contribute to research and innovation around data-centric and model-centric approaches.
• Create, connect, and maintain secure data platforms such as SQL/NoSQL warehouses, object storage, and feature stores.
• Support data privacy, security, and compliance requirements.
• Design REST APIs that expose ML services and data platform features.
• Build and maintain internal tools and web-based interfaces that support ML processes.
• Follow sound engineering practices including version control, code reviews, and clear technical documentation.
• Write reliable, maintainable code and add unit, integration, and performance tests.
• Containerize solutions and set up CI/CD pipelines.
• Maintain and debug ML tooling in production environments.
• Work closely with internal users and ensure alignment with their needs.
• Take full ownership of projects, including stakeholder coordination, requirement analysis, project planning, and technical decision-making.

Requirements

• Several years of hands-on professional experience building ML systems that have delivered measurable business or operational impact.
• Strong command of Python.
• Practical experience in computer vision and ML/DL tooling.
• Solid MLOps knowledge, including data modeling, packaging, containerization, and CI/CD.
• Experience using data science methods to optimize vision-based models.
• Ability to work with TypeScript, REST API development, and modern frontend frameworks such as Vue.js or React, or a willingness to learn them.
• Understanding of cloud and on-premises platforms and integration interfaces.
• Familiarity with model optimization tools such as TensorRT, ONNX, CUDA, and cuDNN, as well as deployment on edge hardware like x64 and ARM/Jetson.

What to Expect

• Modern technologies and AI-led systems.
• A practical engineering setup where your work directly influences machine performance.
• Regular collaboration with international teams.
• An informal workplace with flat hierarchy and fast decision-making.
• Norwegian company culture with an open, direct “you” approach.
• The chance to help shape future machine development.

What We Offer

• Hybrid work arrangement.
• Flexible, international work environment.
• Modern office and testing facilities.
• Exposure to a sustainable, future-focused industry.
• Competitive compensation package with bonus.
• Company laptop and mobile phone.
• Long-term career growth in an international technology company.

Application Notes

If you enjoy tackling complex technical problems and want your software to run on real industrial machines, this role may be a strong fit. Applicants are asked to submit a CV, a short introduction, and a motivation statement in the “Message to manager” field. Applications will be reviewed throughout the posting period, and first-round interviews are expected to begin in early August 2026. The employer states that it is committed to equal opportunity and does not discriminate on the basis of gender, race, ethnicity, religion, color, sexual orientation, identity, disability, age, or other protected status.