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# Senior Computer Vision Engineer, Embodied AI

- **Company:** Niantic, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $229,500 - $255,000
- **Contract:** Permanent contract
- **Skills:** 3D Scanning, Computer Vision, C++ (Programming Language), Cloud Computing, Computer Graphics, Customer Data Management, Distributed Computing Environment, Python (Programming Language), Machine Learning, Regression Testing, Spatial Data Infrastructures, Neural Rendering, Data Processing, Delivery Pipeline, Gaussian, Embodied AI, Low Latency, Operational Systems, Data Pipelines
- **Published:** November 9, 2026
- **Apply:** https://www.juju.com/job/28_25197_f5df470b32bc752fd00f038dcde38a97

## About the Role

* 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., * 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., * Painful sense of urgency. You make decisions and ship as if the company's success depends on it, because it does. A-players love working with you and everyone else struggles to keep up.
 * Intellectually honest. You know there's just one job that underpins every other in a startup: find the truth. You are relentless in your pursuit of it, including when it challenges your own convictions, and do not let your colleagues sleepwalk into failure.
 * Pragmatic. You have no patience for NIH Syndrome and analysis paralysis. You find the fastest path to a working capability without one-way doors that undermine scaling.
 * Strong systems instincts. You understand that production quality includes reliability, observability, cost, latency, maintainability, and usability-not just algorithmic accuracy.
 * Comfort with ambiguity. You can make progress when the problem, data, and requirements are still evolving.

## Description

* Productionize Computer-Vision and 3D Reconstruction Capabilities - Turn research ideas and prototypes into reliable pipeline components and services that can operate across varied customer data and environments.
 * Own End-to-End Output Quality - Ensure that reconstructed environments and spatial artifacts are geometrically coherent, correctly scaled and aligned, and usable by downstream simulation and embodied AI systems.
 * Make Systems Robust to the Real World - Diagnose failures caused by capture quality, scene complexity, scale, calibration, coordinate conventions, and other assumptions that prototypes often leave implicit.
 * Improve Performance, Throughput, and Cost - Profile and optimize GPU and distributed workloads, reduce unnecessary reruns, and help establish the economics of running reconstruction at scale.
 * Build Evaluation and Quality Infrastructure - Create datasets, regression tests, quality gates, and benchmarking tools that help the team measure whether changes improve the system.
 * Shape the Next Generation of Capabilities - Bring production and customer constraints into research planning, helping prioritize the advances that matter most for embodied AI.
 * Work Across the Product Boundary - Partner with Embodied AI product and engineering teams to understand customer requirements and deliver capabilities that fit real training, evaluation, and deployment workflows.
 * Define the Interfaces to Embodied AI Workflows - Help evolve the representations, tooling, and operational systems that allow reconstructed environments to move reliably into customer applications.