AI ML Engineer

What's the role?

As an ML Engineer II, you will help build and operate the platforms, tools, and pipelines that support machine learning throughout its lifecycle. You will work with experienced engineers and researchers to improve automation, scalability, observability, and reliability across production ML systems.You will:

Develop and maintain machine learning pipelines covering data processing, model training, evaluation, deployment, and monitoring.

Support model-serving infrastructure for batch and real-time inference workloads.

Contribute to CI/CD automation, testing, version control, and deployment processes for machine learning applications.

Work with containerized and cloud-native technologies such as Docker and Kubernetes to support scalable ML environments.

Build monitoring and observability capabilities that help identify performance issues, model degradation, and operational risks.

Partner with ML engineers, researchers, and data teams to improve the reliability, reproducibility, and efficiency of machine learning workflows.

Explore and adopt new tools, MLOps practices, and engineering approaches that help accelerate machine learning development and delivery.

Who are you?

You enjoy solving technical problems, learning new technologies, and building scalable systems that help others succeed. You are comfortable working in a collaborative environment and are excited to grow your expertise in machine learning engineering and MLOps.
You will be successful in this role if you bring:

A Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, or a related field.

1-4 years of experience in machine learning engineering, MLOps, platform engineering, software engineering, or a related technical area.

Strong programming skills in Python and a solid understanding of software engineering fundamentals, including testing, code quality, and version control.

Familiarity with machine learning workflows, including model training, evaluation, deployment, monitoring, and retraining concepts.

Experience working with technologies such as Docker, Kubernetes, cloud platforms, CI/CD tools, or infrastructure automation frameworks.

Exposure to workflow orchestration, model lifecycle management, ML platforms, Airflow, MLflow, Kubeflow, DVC, or comparable technologies.

Interest in large-scale data processing, distributed systems, LLMOps, vector databases, or geospatial technologies, along with a willingness to learn and grow in these areas.

What we offer

HERE offers an opportunity to work in a cutting-edge technology environment with challenging problems to solve! You can make a direct impact on delivery of company´s strategic goals and the freedom to decide how to perform your work. We will support you in delivering your day-to-day tasks and achieving your personal goals and developing your skills. Personal development is highly encouraged at HERE. You can take different courses and training at our online Learning Campus and join cross-functional team projects within our Talent Platform.

Who are we?

HERE Technologies is a location data and technology platform company. We empower our customers to achieve better outcomes – from helping a city manage its infrastructure or a business optimize its assets to guiding drivers to their destination safely.

At HERE we take it upon ourselves to be the change we wish to see. We create solutions that fuel innovation, provide opportunity and foster inclusion to improve people’s lives. If you are inspired by an open world and driven to create positive change, join us. Learn more about us on our YouTube Channel.

You will join a team focused on enabling machine learning solutions at scale across HERE. We work closely with data scientists, ML engineers, software engineers, and platform teams to build reliable infrastructure, automate ML workflows, and support the successful deployment of AI-powered products.Our team values collaboration, continuous learning, experimentation, and engineering excellence. Together, we help transform machine learning research into production systems that create real-world impact.