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.
This internship sits at the intersection of systems engineering, mobile application development, and applied research. You'll work directly with platform, research, and application engineers to turn new capabilities in spatial computing, localization, and computer vision into polished experiences for developers and end users — from shared C++ runtime code and native Swift/Kotlin SDKs to production-quality iOS and Android applications.
Depending on your interests and the projects underway, you may work deeper in the platform stack, closer to the application layer, or across both. You'll also use AI-assisted workflows to move quickly across platforms while maintaining high standards for native quality and user experience.
The expected hourly rate for this internship is $48 – $70/hour, based on assessed skills, experience, and relevant coursework. Housing assistance and relocation support available for candidates outside the Bay Area — details provided during the offer process.
This internship is based in our San Francisco, CA office. We work in a high-collaboration environment and ask interns to be on-site a minimum of 4 days per week. To make that easy, we provide lunch every day and snacks are always stocked.
We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed — you may be exactly who we're looking for.
Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process.
I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.
First, our founders brought digital mapping to the world. Next, we invented global-scale AR. Now we're building the real-world foundation model for people, AI and robots.
We're building for the 80% of economic activity that takes place beyond our screens: robots that lose GPS and drift off course, defense teams with no shared 3D picture of where they operate, facilities where downtime costs millions.
Unlike LLMs, which understand language, we build computer-vision models that understand physical space. And unlike generated environments, our models are geometrically accurate and have real-world coordinates - which is what makes physical AI precise and trustworthy.
We work across three capabilities for our customers: reconstructing physical spaces as AI-native digital twins using cheap, off-the-shelf cameras; locating and orienting – “localizing” – machines and people within those spaces; and enabling AI to understand and answer questions about the real world.
