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# AI Engineer

- **Company:** Niantic, Inc.
- **Location:** San Francisco, CA, United States
- **Salary:** $165,600.0 - $221,000.0
- **Contract:** Internship / Graduate position
- **Skills:** Training Data, 3D Scanning, Artificial Intelligence, Computer Vision, Augmented Reality, Extract Transform Load (ETL), Python (Programming Language), Software Deployment, Spatial Data Infrastructures, Digital Twin, Pytorch, Deep Learning, Information Technology, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT, Feature Extraction, Software Coding, Lidar, Data Pipelines, Pose Estimation
- **Published:** October 9, 2026
- **Apply:** https://startup.jobs/ai-engineer-computer-vision-niantic-spatial-10349967

## About the Role

* 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.

## Description

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., * Experience training deep learning models for computer vision tasks in PyTorch - through coursework, research, internships, or early industry work.