Artificial Intelligence Machine Learning Engineer

What's the role?

As AI ML Engineer, you will develop reusable geospatial AI building blocks that enable other teams to create and scale AI-powered capabilities. You will focus on hands-on implementation across embeddings, retrieval, geospatial data preparation and AI pipelines, while working with senior engineers on the broader architecture.

In this role, you will:

• Build reusable geospatial AI components, including embeddings, similarity-search and retrieval capabilities that other domain teams can use and extend.
• Contribute to AI pipelines by implementing and improving detection, validation and data-handling components for map and imagery data.
• Prepare and engineer imagery, map, sensor and traffic data for use in AI and machine-learning workflows.
• Support training, evaluation and fine-tuning of geospatial foundation models in collaboration with senior engineers.
• Develop well-tested, documented components and work with MLOps partners to enable reliable production serving.

Who are you?

You bring a solid foundation in machine learning and software engineering and are interested in applying that expertise to geospatial problems. You enjoy building practical solutions with others while taking ownership of implementation.You bring:

• A Master's degree, or equivalent experience, in Computer Science, Artificial Intelligence, Machine Learning, Geospatial Science or a related discipline.
• Around 2–4 years of ML/AI engineering experience, with exposure to geospatial or imagery data, or strong ML fundamentals and an interest in developing your spatial-domain expertise.
• Strong Python and software engineering fundamentals, including testing, code quality and version control.
• Experience with a deep-learning framework such as PyTorch or TensorFlow.
• Practical experience with embeddings, similarity search or retrieval-based systems, along with familiarity with foundation-model concepts such as self-supervised learning, fine-tuning and transfer learning.
• Experience working with imagery or other high-volume spatial or sensor data.Experience with areas such as multi-sensor imagery, geospatial data formats, distributed training, large-scale data pipelines, agentic or multi-stage AI systems, LiDAR, drone data, TinyML/edge AI, vector databases, or Go/Java/C++ can help you make an impact, but these are not required to apply.

 

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.

You will join AI & Analytics Infrastructure, which brings together Analytics Engineering, AI Infrastructure, Responsible AI and domain AI teams such as AI for Mapmaking. The team develops reusable AI capabilities, including agents, tools, data access and semantics, on a shared infrastructure and control layer. These capabilities help domain teams adopt AI at scale while focusing their expertise on new products and experiences rather than rebuilding common components.