3D Computer Vision Research Engineer, Geospatial Visual Localization

Niantic Spatial is looking for a Research Engineer to help develop Geospatial Visual Localization technologies, which allow people and robots to accurately localize themselves anywhere on the planet. This is an end-to-end applied research role working alongside researchers in 3D vision and machine learning, with work published at leading conferences and shipped into production. The team works with heterogeneous captures from phones, 360 cameras, vehicles, drones, aerial and satellite imagery, LiDAR, and external geographic data. The focus is on accuracy and robustness across urban and rural locations, cameras, seasons, weather, and capture quality, with well-calibrated uncertainty for each pose estimate. The role is based in the London office, with three days per week in office. Base salary is a competitive market rate.

Responsibilities

Design, implement, and evaluate novel visual localization algorithms, combining 3D vision and deep learning; Train large models on multi-modal data at scale, and build the benchmarks that measure their accuracy and robustness; Develop robust solutions for difficult real-world conditions: noisy or denied GPS, changing appearance, varying capture quality, large geographic extents, and imperfect reference maps; Work side-by-side with leading researchers in visual localization, 3D computer vision, and mapping, as well as engineering and product teams, to identify the highest-impact problems and turn research into end-to-end solutions that operate at production scale; Communicate results clearly through technical writing, design reviews, internal presentations, and publications

Requirements

Strong grounding in 3D computer vision, deep learning, robotics, photogrammetry, or a closely related field; Demonstrated experience with multi-view geometry and related problems, such as camera pose estimation, monocular depth estimation, SfM, SLAM, georeferencing, point-cloud registration, or sensor fusion; Knowledge of transformer models for 3D vision, such as vision foundation models, feed-forward 3D reconstruction, or multi-view and multi-modal attention; Knowledge of geospatial data and coordinate reference systems; Ability to formulate ambiguous real-world problems, choose appropriate mathematical or learning-based tools, and evaluate solutions rigorously; Strong Python skills and experience training deep learning models at scale, with libraries such as PyTorch, NumPy, and SciPy; Strong software engineering skills, with the ability to turn research prototypes into reliable, scalable, and maintainable systems; Experience taking an algorithm from research idea and experimental prototype through evaluation, benchmarking, and deployment on real-world data; Clear written and verbal communication and the ability to collaborate across research and engineering disciplines

Nice to have

A PhD in computer vision, robotics, machine learning, photogrammetry, geodesy, remote sensing, or a related field, or equivalent research or industry experience (not required); A strong publication record or substantial active involvement in the research community, especially at venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, SIGGRAPH, SIGGRAPH Asia, 3DV, ICRA, RSS, or related conferences and journals; Experience with aerial or satellite imagery, GNSS/GPS, IMUs, LiDAR data, geodesy, mapping systems, or large-scale spatial data; Evidence of technical leadership, mentorship, or successful cross-functional delivery

Benefits

Annual bonus; equity; comprehensive benefits package