Job Description

What's the role?

In this role, you will partner closely with ML engineers, researchers, data teams, and platform teams to design scalable infrastructure, automate deployment workflows, and establish engineering standards that ensure reliability, observability, and reproducibility across the machine learning lifecycle.

• Design and implement end-to-end machine learning pipelines covering data ingestion, training, evaluation, deployment, monitoring, and retraining.
• Develop scalable infrastructure that enables consistent and repeatable movement of models from research to production.
• Own model-serving architectures for both batch and real-time inference workloads.
• Establish CI/CD practices for machine learning, including automated testing, model packaging, version control, and deployment automation.
• Build and maintain containerized and orchestrated environments using technologies such as Docker and Kubernetes.
• Optimize infrastructure utilization for compute and GPU-intensive workloads while balancing performance and cost efficiency.
• Implement model and data versioning, reproducibility standards, and rollback mechanisms.
• Develop monitoring, alerting, and observability frameworks for production ML systems.
• Implement mechanisms for detecting data drift, model degradation, latency issues, and operational risks.
• Support continuous feedback loops, human-in-the-loop workflows, and retraining processes that improve model quality over time.
• Translate complex operational challenges into scalable, secure, and maintainable platform solutions.
• Evaluate emerging MLOps technologies, orchestration frameworks, and industry best practices to guide tooling decisions.

Who are you?

You bring a strong combination of machine learning infrastructure expertise, software engineering excellence, and a passion for building reliable production systems. You have a Master's degree or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, or a related field, along with demonstrated experience delivering machine learning systems into production environments at scale.

• 4+ years of experience in MLOps, ML platform engineering, or ML infrastructure engineering.
• Deep expertise across the machine learning lifecycle, including training, evaluation, deployment, monitoring, and retraining.
• Strong experience implementing CI/CD pipelines, automated testing, model packaging, and release management for ML systems.
• Hands-on proficiency with Docker, Kubernetes, and cloud platforms such as AWS, Azure, or GCP, including infrastructure-as-code practices.
• Experience building scalable model-serving solutions supporting both batch and real-time inference workloads.
• Strong knowledge of observability, monitoring, data drift detection, model validation, and operational excellence.
• Proficiency in Python and strong software engineering fundamentals, including testing, code quality, and version control.
• Experience with workflow orchestration and MLOps platforms such as MLflow, Kubeflow, Airflow, DVC, or comparable technologies.
• Practical experience supporting large-scale data processing environments, distributed computing, streaming architectures, or Spark-based systems.
• Familiarity with LLMOps practices, retrieval infrastructure, vector databases, and operationalization of AI-powered systems.
• The ability to make informed architectural decisions, lead technical initiatives, and collaborate effectively across multidisciplinary teams.
• Excellent communication, mentoring, and stakeholder engagement skills.

Exposure to geospatial platforms, spatial data infrastructure, edge AI, TinyML, LiDAR, drone data processing, Go, Java, or C++ is valuable in helping accelerate impact within HERE’s innovation ecosystem.

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.

HERE is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, age, gender identity, sexual orientation, marital status, parental status, religion, sex, national origin, disability, veteran status, and other legally protected characteristics.

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.

The Technology Innovation Lab is a cross-disciplinary team advancing artificial intelligence, intelligent automation, and next-generation spatial technologies across HERE. We bring together researchers, machine learning engineers, software engineers, and platform specialists to develop and scale innovative solutions spanning Agentic AI, Generative AI, representation learning, and foundational machine learning systems. Together, we transform emerging ideas into production-ready capabilities that create measurable business value.