AI Engineer, Computer Vision

About Niantic Spatial

AI has learned from almost everything online, but it still struggles with the physical world, where 99.5% of economic activity happens. It doesn’t know where things are, how spaces fit together, or what changes over time.

That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are.

In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right.

Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us.

About the Team

Localization is where the map meets the moment. Our localization team builds the living geospatial world model and the centimeter-level Visual Positioning System (VPS) that lets people and robots know exactly where they are, from any camera, almost anywhere on Earth.

That system is learned, not hand-built. Our localization stack has moved from classical geometry to visual models trained on billions of posed images — models that have to be accurate to the centimeter, robust to lighting and seasons and clutter, and fast enough to run in production for every query. This team sits at the junction of research and production: we take what works in a paper, make it work on our data, and make it work for customers.

About the Role

We're hiring an AI Engineer, Computer Vision, to contribute to the deep learning models at the heart of our localization system. Working alongside experienced researchers and engineers, you'll help train and iterate on the visual models — feature extractors, matchers, pose regressors, and retrieval models — that turn a single image into a precise position in the world.

This is a full-loop environment. You'll get exposure to the entire pipeline: the data the model learns on, training, evaluation, and the service that runs it in production. 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 — and you'll grow into that rigor quickly.

What You'll Do

• Train and Evaluate Models —
• Implement, 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 real production captures.
• Build Data Pipelines —
• Contribute to high-throughput ingestion and preprocessing pipelines that turn raw imagery, poses, and 3D point clouds into training-ready datasets, including curation, sampling, and augmentation.
• Run Experiments at Scale —
• Run training jobs across GPU environments on our 30-billion-image corpus, working with senior engineers and ML Infrastructure to keep experiments reproducible and results interpretable.
• Extend the Benchmark —
• Help build and maintain the evaluation suite for localization — pose accuracy, recall across lighting and season changes, and latency under production load — so results are comparable across model versions.
• Support Production Deployment —
• Work with senior engineers to take models from checkpoint toward serving: export, inference optimization, and monitoring once live. Learn what it means to own a model end-to-end, not just hand off a checkpoint.
• Bridge Research and Product —
• Work with the Research team and Real-World Test Lab to understand how field failures translate into training data and benchmark improvements.

What You'll Bring

• 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 the ability to read and reason about training runs — identifying whether a bottleneck is in data loading, compute, or model architecture.
• Strong Python skills and a habit of writing code that others can rerun and build on.
• A bachelor's or master's degree in computer science, computer vision, robotics, or a 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.