San Francisco, California, United States · On-site
$50/hr–$65/hr
About Niantic Spatial 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 inte…
Skills: Python, Deep Learning, PyTorch, JAX, Isaac Sim
San Francisco, California, United States · Hybrid
$155k–$170k/yr
About Niantic Spatial 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 inte…
Skills: Business operations, Legal operations, Finance operations, Procurement, Program management
San Francisco, California, United States · Hybrid
$158k–$210k/yr
About Niantic Spatial 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 inte…
Skills: Financial Modeling, FP&A, Budgeting, Forecasting, Variance Analysis
San Francisco, California, United States · Hybrid
$171k–$215k/yr
About Niantic Spatial 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 inte…
Skills: Solutions Architecture, Cloud Computing, Edge Computing, Enterprise Integration, API Design
San Francisco, California, United States · Remote OK
$171k–$215k/yr
About Niantic Spatial 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 inte…
Skills: Solutions Architecture, Systems Engineering, Technical Pre-sales, Cloud Architecture, Hybrid Deployment
San Francisco, California, United States • On-site
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You will design, implement, and deploy learning-based systems on physical robotic hardware to advance autonomous locomotion, navigation, and control. Additionally, you will work across simulation environments and physical platforms to close the sim-to-real gap and perform hardware debugging and sensor calibration.
Candidates must be currently enrolled in or a recent graduate of a BS, MS, or PhD program in Robotics, Computer Science, Electrical Engineering, or a related field. Proficiency in Python, deep learning frameworks, and hands-on experience with simulation and physical hardware are required.
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.
As a Robotics Engineering Intern or Co-op at Niantic Spatial, you will work directly at the intersection of learning-based systems and physical hardware. You will work hands-on with physical hardware, push robots toward fully autonomous operation across locomotion, navigation, and control, and iterate rapidly based on real-world feedback.
Robot Learning Systems — Design, implement, and deploy learning-based systems on physical robotic hardware, working across locomotion, navigation, and control to push robots toward full autonomy.
Sim-to-Real Deployment — Work across simulation environments (Isaac Sim, MuJoCo) and physical platforms, closing the sim-to-real gap and validating system behavior under real-world conditions.
Hardware Iteration — Debug hardware, perform sensor calibration, and fix edge cases under real deployment conditions
Approach Evaluation — Evaluate and weigh tradeoffs between classical robotics and learning-based methods, choosing the right tool for each problem and contributing to technical decision-making on the team.
Currently enrolled in (or a recent graduate of) a BS, MS, or PhD in Robotics, Computer Science, Electrical Engineering, Mechanical Engineering, or a related field.
Strong proficiency in Python and modern deep learning frameworks (PyTorch or JAX).
Hands-on experience with simulation environments (Isaac Sim, MuJoCo) and physical robotic hardware.
Ability to work on-site full-time in San Francisco, CA for the duration of the program. Relocation or housing stipends are not included in this fall program.
Can take a robotics system from 0 to 1 and iterate quickly based on real world feedback.
Comfortable working with hardware - sensor calibration, hardware debugging, and fixing edge cases under tight deadlines.
Ability to weigh trade-offs between classical robotics and learning-based approaches
High intellectual curiosity and a focus on pushing systems beyond existing baselines.
Passionate about robotics and love what you do because you will be doing a lot of it.
· Term — 12 to 16 weeks, full-time commitment.
· Location — 100% on-site in San Francisco, CA.
· Role Type — Open to undergraduate, masters, and PhD students, as well as co-op program participants.
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.
We build real-world foundation models that unlock physical AI for a more productive and resilient world.
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.
We build real-world foundation models that unlock physical AI for a more productive and resilient world.
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.
