About the RoleAI & Analytics Infrastructure brings together Analytics Engineering, AI Infrastructure, Responsible AI, and domain AI teams (e.g., AI for Mapmaking) to provide reusable building blocks agents, tools, data access, semantics that run on a shared infrastructure and control layer (agentic frameworks, agentic identity, guardrails). This unlocks fast, scalable, and safe AI adoption, and lets domain teams focus on innovation rather than re-building operational plumbing each time.
This role builds the geospatial-specific building blocks themselves embeddings, retrieval, and data-access components that power systems . Without this role, that build capacity falls to senior/lead engineers who should be focused on architecture rather than day-to-day implementation and the pace at which new geospatial capabilities reach the shared layer slows down. A Geo AI Engineer II supplies that build capacity now, while developing the geospatial-AI depth this central function will keep needing as more domain teams plug into the shared layer.
What You'll Do
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- Build reusable geospatial AI building blocks embeddings, similarity search, and retrieval components that other domain teams consume rather than rebuild.
- Contribute to production agentic pipelines , implementing and improving detection, validation, and data-handling components that process real map and imagery data.
- Prepare and engineer geospatial data (imagery, map data, sensor/traffic signals) as AI feature-engineering input framed as an AI capability, not classic GIS infrastructure.
- Support training, evaluation, and fine-tuning work for geospatial foundation models under senior engineers' architectural direction.
- Hand off working components cleanly to MLOps for production serving, with enough documentation and testing that another team can build on them without you in the room.
Who are you?
- Master's degree (or equivalent experience) in Computer Science, AI, Machine Learning, Geospatial Science, or a related field.
- 2–4 years of experience in ML/AI engineering, ideally with some hands-on exposure to geospatial or imagery data or strong ML fundamentals with a genuine aptitude and interest in ramping into the spatial domain.
- Solid Python and software engineering fundamentals: testing, code quality, version control.
- Collaborative by default comfortable taking direction from senior/lead engineers on architecture while owning your own implementation.
Required
- Experience with at least one deep learning framework (PyTorch or TensorFlow).
- Practical experience with embeddings, similarity search, or retrieval-based systems.
- Working familiarity with foundation-model concepts self-supervised learning, fine-tuning, transfer learning.
- Comfortable working with imagery or other high-volume spatial/sensor data.
Preferred
- Exposure to multi-sensor imagery (optical, infrared, radar/SAR) and common geospatial data formats.
- Experience with distributed training or large-scale data pipelines.
- Some exposure to agentic or multi-stage pipeline systems (not necessarily production-scale).
Nice to HaveExposure to LiDAR, drone data processing, TinyML/edge AI, vector databases, or Go/Java/C++ — useful for accelerating impact inside HERE's mapmaking-AI ecosystem, though not required to apply.
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.
