Senior Computer Vision Engineer, Embodied AI
Senior Computer Vision Engineer on Niantic Spatial's Embodied AI team, turning 3D reconstruction and spatial-perception research into reliable real-to-sim pipelines for embodied AI customers — owning output quality, robustness, and GPU/distributed performance. Core stack: Python, C++, CV/ML, photogrammetry/SLAM, Gaussian splatting, cloud GPU workloads.
- Compensation
- $229.5K–$255K / yr
- Location
- San Francisco, CA
- Work setup
- Hybrid
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- Significant experience building and operating production computer-vision, machine-learning, graphics, or data-processing systems
- Hands-on experience with 3D reconstruction, photogrammetry, structure from motion, SLAM, mapping, scene understanding, computer graphics, or a related field
- Experience delivering reliable computer-vision or 3D systems used by other teams or customers
- Practical command of 3D and spatial data, including geometry, camera models, coordinate frames, calibration, and metric scale
- Strong Python skills, with the ability to work in C++ or other performance-oriented environments when needed
- Experience with cloud infrastructure, GPU workloads, distributed processing, or large-scale data pipelines
- A bachelor's degree in a relevant field, or equivalent experience
Nice to have
- Experience with Gaussian splatting, neural rendering, meshing, or related reconstruction methods
- Built evaluation or benchmarking infrastructure for perception or reconstruction systems
- Experience with robotics, simulation, or embodied AI applications
- Experience optimizing large-scale GPU workloads for cost, throughput, or latency
Full description
About Niantic Spatial
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.
About the Role
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 Senior Computer Vision Engineer to join our Embodied AI team, focused on 3D scene understanding and semantics. A reconstructed environment is only useful to a robot once it knows what is in it: which surfaces are floor, which objects can be grasped, moved, or opened, where one room ends and another begins. You'll work hand in hand with our Research Scientists to take advances in open-vocabulary perception, 3D semantic labeling, and scene graph construction and turn them into reliable capabilities that embodied AI teams can use in production.
This is an applied research role. You'll live at the boundary between a research prototype and a production pipeline: adapting methods so they hold up on messy customer captures, lifting 2D semantic signals into geometrically consistent 3D, defining what "semantically correct" means for a downstream policy, and feeding what you learn back into the research agenda. You understand that the difficult part is often not the core method, but the messy inputs, edge cases, operational constraints, and downstream requirements surrounding it. You care about quality, cost, latency, and reliability, and you know that a system is only successful when people can depend on it. This is a hands-on engineering role for someone who wants to build, ship, and own outcomes. You'll help shape both the technology and the product, while remaining close to the code and the problems our customers are trying to solve.