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Salary: USD 165,600 - 221,000 / annual
Niantic Spatial is building the future of physical AI, powered by a proprietary database of over 30 billion posed images. The company's groundbreaking mapping technology enables spatial intelligence for robotics, public sector, and energy/industrial markets.
The Localization team builds the living geospatial world model and centimeter-level Visual Positioning System (VPS) that enables precise positioning from any camera almost anywhere on Earth. The system is learned, not hand-built, using visual models trained on billions of posed images that must be accurate to the centimeter, robust to lighting/seasonal changes, and fast enough for production.
As an AI Engineer, Computer Vision, you will contribute to the deep learning models at the heart of the localization system. You'll work alongside experienced researchers and engineers to train and iterate on visual models—feature extractors, matchers, pose regressors, and retrieval models—that turn a single image into a precise position.
Key responsibilities:
- Train and evaluate visual localization models in PyTorch: local feature extraction and matching, image retrieval, pose regression, and scene-coordinate regression. Iterate on architectures, loss functions, and training loops to improve accuracy on production captures.
- Build high-throughput data pipelines that turn raw imagery, poses, and 3D point clouds into training-ready datasets, including curation, sampling, and augmentation.
- Run training jobs across GPU environments on the 30-billion-image corpus, working with senior engineers and ML Infrastructure to keep experiments reproducible and results interpretable.
- Build and maintain the evaluation suite for localization—pose accuracy, recall across lighting and season changes, latency under production load—ensuring results are comparable across model versions.
- Support production deployment: export models, optimize inference, and monitor live systems. Learn to own a model end-to-end.
- Bridge research and product by working with the Research team and Real-World Test Lab to understand how field failures translate into training data and benchmark improvements.
This is a full-loop environment with exposure to the entire pipeline: data, training, evaluation, and production service. You'll be surrounded by people who care deeply about correctness—where a loss curve going down is not the same as a system getting better.
REQUIREMENTS:
- Experience training deep learning models for computer vision tasks in PyTorch (through coursework, research, internships, or early industry work).
- Foundational understanding of at least one of: visual localization, learned feature matching, image retrieval, pose estimation, SLAM, or structure-from-motion, and the 3D geometry underneath them (camera models, epipolar geometry, pose parameterizations).
- Comfort working with large image or 3D datasets—preprocessing, curation, and understanding how data quality affects model behavior.
- Familiarity with GPU-based training and ability to read and reason about training runs—identifying bottlenecks in data loading, compute, or model architecture.
- Strong Python skills and a habit of writing code that others can rerun and build on.
- Bachelor's or master's degree in computer science, computer vision, robotics, or related field, or equivalent practical experience.
NICE TO HAVE:
- Published or contributed to work on learned localization, visual place recognition, scene-coordinate regression, or vision foundation models (CVPR, ECCV, ICCV, NeurIPS, 3DV, or equivalent).
- Experience with large-scale self-supervised or multi-view pretraining, or fine-tuning vision foundation models for geometric tasks.
- Exposure to inference optimization for edge or mobile hardware (TensorRT, ONNX, CoreML, or similar).
- Experience with petabyte-scale spatial data: point clouds, meshes, posed image collections, or geospatial indexing.
- Background in robotics, augmented reality, 3D reconstruction, digital twins, or autonomous systems.
- Prior industry or research internship experience shipping or contributing to a production CV system.
