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.
Niantic Spatial makes the physical world computable, helping people and machines collaborate safely by aligning how they understand reality. One of the fundamental problems we're helping autonomy teams and engineers overcome is the sim-to-real gap for visual-spatial understanding. We're applying our team's decades of experience encoding the world precisely as it is at Google Maps, Google Earth, and Niantic Labs to build the scalable real-to-sim stack for embodied AI.
We're looking for a Staff Engineer, Robot Learning to lead engineering for our real-to-sim product within the Embodied AI group. One of the fundamental problems facing autonomy teams is the sim-to-real gap in visual-spatial understanding, and we're applying decades of experience encoding the world precisely as it is to build the scalable real-to-sim stack for embodied AI.
Working alongside the group's General Manager and Product leader, you'll inform the roadmap, own the technical requirements our researchers and engineers build against, and personally prototype and evaluate the changes you propose. You've lived the problems our customers are solving: you've trained and deployed embodied AI systems, seen where and why simulation fails to predict real-world performance, and worked through the consequences.
This is not a role for someone who wants to manage and not ship, or someone who wants to ship without ownership of commercial consequences. This is a role for an entrepreneurial tech lead who loves to build and is energized by helping those around them do the best work of their lives. You have a co-founder mentality. You may have been part of a founding team before, or want to start your own business one day.
The expected salary range for this role is $270,000 – $300,000 per year. Compensation also includes an annual bonus, equity, and a comprehensive benefits package including medical, dental, and vision coverage, 401(k), and more.
This role is based in our San Francisco office, with three days per week in office.
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.
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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.
