AI Engineer, Computer Vision

Niantic Spatial•San Francisco, CA

1d•$165,600 - $221,000•Hybrid

About The Position

At Niantic Spatial, we're building the future of physical AI. Powered by a proprietary database of over 30 billion posed images, our groundbreaking mapping technology unlocks a new dimension of interaction and spatial intelligence that helps both humans and machines better understand, represent, navigate, and engage with the real environment. Our reconstruction technology captures environments with geometric accuracy and extreme detail from any standard camera, and our Visual Positioning System delivers precise positioning almost anywhere in the world. We serve customers across robotics, the public sector, and energy and industrial markets — building for the 80% of economic activity that takes place beyond our screens. 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. 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.

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 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 Haves

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.

Responsibilities

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.
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 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.
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.
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.
Work with the Research team and Real-World Test Lab to understand how field failures translate into training data and benchmark improvements.