Careers at Niantic Spatial

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 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. About the Role We're hiring software engineering interns for Summer 2027; each intern will be embedded in a specialized track. You won't be shadowing or running demos - you'll own a real problem on a small team, write code that ships to production, and work on systems and datasets that don't exist anywhere else: petabyte-scale 3D reconstructions, foundation models trained on physical space, and positioning infrastructure deployed globally. Select your primary track when you apply. We'll match you based on fit; switching tracks after matching is uncommon but possible if there's strong mutual interest. Track 1: ML /AI Infrastructure You'll build the systems that make large-scale physical AI possible. Not just run experiments, but design the distributed training pipelines, data ingestion infrastructure, and GPU optimization layers that let us train foundation models on billions of images and 3D data points. Model Training with PyTorch — Implement, train, and evaluate model architectures in PyTorch, iterating on data loaders, loss functions, and training loops to improve model quality and convergence on real production datasets. Training Infrastructure — Design and scale distributed training pipelines for our Large Geospatial Model, handling petabyte-scale spatial data across multi-GPU and multi-node environments. Data Pipelines — Build high-throughput ingestion and preprocessing pipelines that transform raw imagery and 3D point clouds into training-ready datasets. Observability — Instrument training runs with metrics, dashboards, and alerting so engineering teams can debug and iterate faster. Best for: Students obsessed with the intersection of ML and systems - PyTorch, distributed computing (Ray, Spark), CUDA, and high-performance architecture. Track 2: Product Engineering You'll build the product APIs and services that sit behind our spatial computing products, helping customers manage projects, organize spatial data, upload content, and turn it into useful experiences. You'll work across backend and product engineering to take features from design to production, with a focus on clear interfaces, reliable workflows, and software that other teams can build on. Product Features — Build and ship features for managing organizations, projects, spatial data, and content. API Design — Design the interfaces that connect our products, web and mobile applications, and developer tools, making them consistent, well-documented, and easy to use. Workflows & Integrations — Connect the steps behind core product workflows, from uploading and processing data to making results available to users and applications. End-to-End Delivery — Collaborate with engineers across frontend, backend, and infrastructure to launch features and learn from real customer use. Best for: Students who enjoy building products from the backend up and interested in how APIs and data power user experiences. Motivated by shipping software that real customers and developers use. Track 3: Backend Systems You'll build and harden the high-throughput services that power our Visual Positioning System and reconstruction platform - processing vast volumes of visual data and delivering centimeter-level positioning to users and robots globally. Service Development — Design and ship microservices in Go or C++ that sit in the critical path of our positioning and reconstruction APIs. Data Ingestion — Build and optimize pipelines that ingest, validate, and route large volumes of visual and sensor data from diverse hardware sources. Performance & Reliability — Profile service bottlenecks, improve latency, and improve system observability through structured logging, metrics, and distributed tracing. API Design — Contribute to internal and external API design, writing clean, well-tested, production-grade interfaces that other teams and customers depend on. Best for: Pragmatic engineers who care about correctness, performance, and clean systems design and want to see their code serving real traffic within weeks. What You'll Bring (All Tracks) Currently pursuing a BS or MS in Computer Science, Robotics, Electrical Engineering, Systems Engineering, Computer Vision, or a related field. TypeScript, Python, C++ or Go experience for Infrastructure and Backend tracks. Genuine curiosity about how AI interacts with the physical world - 3D reconstruction, spatial reasoning, or real-world positioning systems. Ability to work independently, debug ambiguous problems, and communicate clearly with a small team. Available for 4 days per week in our San Francisco office for the full 12-week internship. Nice to Have Experience with cloud infrastructure - Kubernetes, AWS or GCP, Docker, Terraform. Familiarity with CUDA or GPU parallelization. Open-source contributions to libraries like PyTorch, OpenCV, COLMAP, or similar. Prior work in robotics, autonomous systems, XR, or spatial computing (coursework, research, or projects acceptable) Exposure to Gaussian Splatting, NeRF, or 3D reconstruction techniques Familiarity with REST APIs Experience with SQL or other relational databases Experience using AI-assisted development tools (Claude, ChatGPT, etc.) to write, debug, and improve code Compensation & Benefits The expected hourly rate for this internship is 48–70/hour, based on assessed skills, experience, and relevant coursework. Housing assistance is available for candidates outside the Bay Area - details provided during the offer process. Location & Work Model 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. Inclusive Application 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. Equal Opportunity 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. Candidate Privacy 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 .

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 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. About the Real-World Test Lab (RWTL) Physical AI doesn't get graded on a leaderboard. It gets graded on a factory floor at shift change, in a substation with no GPS, on a site where the lighting is wrong and the stakes are real. The Real-World Test Lab closes the gap between the benchmark and the field. We bring the customer's world inside our walls — their devices, their environments, their hardest conditions, and their definition of success — and make it the bar every release has to clear. As Niantic Spatial's first and most demanding customer, we push our reconstruction, localization, and spatial understanding to their limits, find where they shine and where they break, and turn that into evidence that shapes what we build next. It's a new team at the frontier of physical AI, and you'll help invent how the job is done. About the Role We're hiring a Hardware Operations Engineer with a robotics specialism, reporting to the Director of the Real-World Test Lab, to own the physical layer of our evidence. You'll manage our fleet of capture devices and robots, engineer the test environments and the instrumentation inside them, support the captures our data programs depend on, and run physical test sessions, including the real-robot side of our sim-to-real work. A physical run only counts as evidence if the hardware is characterized, the environment is controlled, the ground truth is trustworthy, and someone can reproduce the run months later. You'll build what makes that possible: fixtures, ground-truth references, calibration and data-offload tooling, and the integrations that feed physical runs into the same automated evaluation system as everything else. You engineer the conditions, not just the setup. When a physical result didn't match expectations, you've worked out whether the hardware, calibration, environment, or system under test was responsible. Then you built the rig, reference, or logging that kept the question from coming back. You know an uninstrumented physical test is an anecdote, and in the Lab you'll be the person who can say with authority whether a strange result is real. What Success Looks Like 30 days: Fleet inventory and characterization baseline complete, safe operating procedures defined for current spaces, and first instrumentation gaps identified. 60 days: First physical robot run executed with ground-truth instrumentation, documented well enough to repeat, with results in the Lab's evaluation system. 90 days: Test environments in regular use with controlled, documented conditions; capture support running smoothly with our data acquisition team; and the physical side of sim-to-real running on a standing instrumented protocol. What You'll Do Own and Characterize the Fleet - Manage our capture devices, sensors, and robots end to end: inventory, calibration, firmware, maintenance, logistics, and spares. Characterize what each platform can and cannot do, so a device's limits are a known quantity rather than a surprise inside a result. Engineer the Physical Test Environments - Design, build, and operate the spaces we test in, covering layout, lighting control, markers, obstacle configurations, and survey-grade ground truth references, across our office, rented or workplace-services-supported space, and partner or customer sites. Make each environment a controlled variable rather than an improvisation. Build the Instrumentation - Create the rigs, mounts, references, and measurement setups that turn a physical run into quantitative data: trajectory ground truth, positional accuracy references, timing and synchronization across sensors, and automated condition logging. Integrate Physical Runs Into the Evaluation System - Work with the AI Automation Engineer so physical results flow into the same scorecards and comparisons as software-only evaluations, through automated data offload, run metadata, and structured condition capture rather than a spreadsheet and a folder of files. Run the Physical Test Sessions - Execute robot runs and device-in-the-loop tests: robot bring-up, teleoperation, scripted trials, and the repetition that makes a result statistically meaningful rather than anecdotal. Own the Physical Half of Sim-to-Real - When a scenario has been evaluated in simulation, engineer and execute its physical counterpart so the comparison is valid, matching environment, route, lighting, sensor configuration, and robot setup, and instrumenting the run so divergence is explainable rather than mysterious. Own Safety - Define and maintain safe operating procedures for robots and powered equipment in our test spaces, and coordinate with workplace services, IT, Legal, and site owners on access and safety requirements. Automate Your Own Work - Script the repetitive parts, including configuration, calibration checks, data offload, and condition logging, so setup time falls and a colleague can run a session without you. What You'll Bring Hands-on engineering experience with robotic hardware, covering bring-up, integration, teleoperation, troubleshooting, and maintenance. Mobile or wheeled platforms preferred. Built test rigs, fixtures, or instrumentation that produced quantitative measurements of a physical system's behavior. Designed and run structured physical experiments, including ground truth methodology, and documented conditions rigorously enough for someone else to reproduce them. Worked with sensor calibration, multi-sensor synchronization, and coordinate frame conventions, and understand how errors in each propagate into a result. Strong Python and command-line proficiency, with a track record of automating hardware configuration, data offload, or test execution. Managed hardware fleets operationally: inventory, firmware, calibration schedules, logistics, and repair coordination. Practical safety experience operating powered equipment or robots around people. A bachelor's degree in a relevant field, or equivalent experience. Nice to Have Worked with ROS or ROS 2, including writing nodes or integrating sensor drivers. Operated NVIDIA Isaac Sim or Isaac Lab as a user, running scenarios someone else authored. Worked with professional capture hardware such as terrestrial laser scanners, NavVis or Leica systems, LiDAR, 360 cameras, or drones. Established survey-grade or motion-capture ground truth for localization or navigation evaluation. Supported photogrammetry, Gaussian splat, or 3D reconstruction capture workflows. Run test operations at customer or partner sites, with the coordination and discretion that requires. Competencies Engineering judgment about physical systems. You reason about error sources: calibration drift, timing offsets, mounting rigidity, lighting variation, floor surface. When results disagree, you form a hypothesis and design the measurement that settles it. Hands-on and unfazed. You'd rather be in the space with the robot than reading about it. Cables, mounts, batteries, firmware, and a session that starts at 7am are all part of the job. Meticulous about conditions. You instrument rather than remember. You know the value of a physical test lives in how precisely its conditions were captured. Safety-minded without being slow. You take real responsibility for operating robots around people, and you build procedures where the safe path is also the fast path. Builds for other people. Your fixtures, scripts, and procedures get used by colleagues without you in the room. Comfortable in an unfinished environment. Much of this does not exist yet. You'll be selecting equipment, designing the first rigs, and defining how a space gets configured, with support but without a playbook. Compensation & Benefits Base salary range of $142,200 to $193,000. Compensation also includes an annual bonus, equity, and a comprehensive benefits package including medical, dental, and vision coverage, 401(k), and more. Location & Work Model This role is based in our San Francisco office, with three days per week in office. Inclusive Application 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. Equal Opportunity 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. Candidate Privacy 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 .

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 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. 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. What You'll Do Productionize 3D Scene Understanding Capabilities - Turn research prototypes in open-vocabulary segmentation, 3D semantic and instance labeling, affordance prediction, and scene graph construction into reliable pipeline components and services that operate across varied customer data and environments. Own End-to-End Output Quality - Ensure that reconstructed environments carry labels, instances, and relationships that are consistent, complete, and correct enough for downstream simulation and policy learning, and define what "correct enough" means for each customer workflow. Bridge Research and Production - Work day to day with the Embodied AI Research Scientist: pressure-test new methods on real customer data, identify where they break, and bring production and customer constraints back into research planning to prioritize the advances that matter most. 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. Build Evaluation and Quality Infrastructure - Create datasets, regression tests, quality gates, and benchmarking tools that help the team measure whether changes improve the system. 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. 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. Help evolve the representations, tooling, and operational systems that allow reconstructed environments to move reliably into customer applications. What You'll Bring 5+ years of computer vision experience and a bachelor's degree or equivalent, Significant experience building and operating production computer-vision, machine-learning, graphics, or data-processing systems. Deep hands-on experience with scene understanding: semantic, instance, or panoptic segmentation; open-vocabulary detection and segmentation; or vision-language models applied to perception. Experience taking 2D perception into 3D, such as multi-view label fusion, 3D semantic segmentation, instance tracking across views, or scene graph construction over reconstructed geometry. Experience delivering reliable computer-vision or 3D systems used by other teams or customers, including the evaluation that proved they worked.. Practical command of 3D and spatial data, including geometry, camera models, coordinate frames, calibration, and metric scale. Strong Python and PyTorch 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. Nice to Have Hands-on experience with 3D reconstruction, photogrammetry, structure from motion, Gaussian splatting, neural rendering, or meshing. Experience with SLAM, visual positioning, localization, or large-scale mapping systems. Experience with robotics, simulation, or embodied AI applications, in particular how semantic labels are consumed by a policy or a simulator. Built evaluation or benchmarking infrastructure for perception or reconstruction systems. Experience optimizing large-scale GPU inference workloads for cost, throughput, or latency. A publication record or contributions to widely used open-source perception codebases. Advanced degree in computer vision, robotics, machine learning, or a related field. Competencies Relentless bias for action. You make decisions and ship as if the company’s success depends on it, because it does. You set the pace, and drive the people around you to rise to it. 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 you raise concerns when you see them — even when it's uncomfortable, and especially when it's unpopular. Pragmatic. You have no patience for not-invented-here 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. Compensation & Benefits Base salary range of $229,500 to $255,000 per year. Compensation also includes an annual bonus, equity, and a comprehensive benefits package including medical, dental, and vision coverage, 401(k), and more. Location & Work Model This role is based in our San Francisco office, with three days per week in office. Inclusive Application 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. Equal Opportunity 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. Candidate Privacy 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 .

At Niantic Spatial, we’re building the future of geospatial AI. Powered by a proprietary database of over 30 billion posed images and a groundbreaking third-generation digital map, our mission is to develop spatial intelligence that helps both humans and machines better understand, navigate, and engage with the physical world. Our high-fidelity mapping technology unlocks a new dimension of interaction—laying the foundation for AI to truly comprehend and operate within real-world environments. Join us as we build a living model of the world that people and machines can talk to. As a Tech Lead for the Applied Computer Vision Algorithms Team, you’ll help drive our “Reconstruct”, “Understand” and “Localization” capabilities. This team is responsible for creating the high-fidelity visual and semantic maps—specifically textured, semantic meshes, and Gaussian Splats— as well as localization maps that allow our Large Geospatial Model (LGM) to perceive the world with human-like precision. Closely working with the R&D and product teams your work will bridge the gap between cutting-edge theory and real-world utility, turning complex geospatial data into a persistent sense of space for the next generation of AI and robotics. Job Responsibilities Applied Research & Implementation: Actively translate top-tier research papers (e.g., from CVPR, ECCV, NeurIPS) into production-grade features within our tech stack. Technical Leadership: Lead the design and implementation of 3D reconstruction pipelines, focusing on Structure from Motion (SfM) and high-fidelity mesh generation as well as 3D gaussian splats. Algorithm Optimization: Develop and optimize Gaussian Splatting quality algorithms and general ML code for high-performance execution on CPU and GPU. Production Implementation: Write and maintain high-performance, shader-based production code in C++ for Android and Linux environments. Technical Strategy & Mentorship: Work with engineering leadership to define the technical roadmap and quarterly objectives for the Applied CV Team; provide high-level mentorship and code governance to elevate the team’s technical bar. Cross-Functional Collaboration: Partner with the Research and Spatial Solutions teams to turn strategic goals into actionable plans. Quality & Benchmarking: Drive decision-making creating high quality data that allows the accurate spatial grounding of AI queries with structural, semantic and location specific knowledge. Job Requirements Years of Experience: 8+ years of professional experience in Computer Vision, Machine Learning, or a related field (or 6+ years with a PhD in a relevant domain). Education: Bachelor’s degree in Computer Science, Engineering, or a related technical field; Master's or PhD preferred. Core Technical Skills: Strong proficiency in C/C++ and Python for production-level software development. Specialized Expertise: Proven experience in 3D Computer Vision/ML, specifically with Structure from Motion (SfM), 3D reconstruction, and Gaussian Splatting rendering techniques. Hardware Optimization: Demonstrated ability to optimize algorithms for GPUs in Android or Linux environments. Graphics Knowledge: Experience with computer graphics and C++ shader-based implementations. Technical Leadership Experience: Previous experience tech leading a team of computer vision engineers in a high-growth environment. Work Location: This position requires 3 days per week in our San Francisco OR Sunnyvale office.

At Niantic Spatial, we’re building the future of geospatial AI. Powered by a proprietary database of over 30 billion posed images and a groundbreaking third-generation digital map, our mission is to develop spatial intelligence that helps both humans and machines better understand, navigate, and engage with the physical world. Our high-fidelity mapping technology unlocks a new dimension of interaction—laying the foundation for AI to truly comprehend and operate within real-world environments. Join us as we build a living model of the world that people and machines can talk to. As a Tech Lead for the Applied Computer Vision Algorithms Team, you’ll help drive our “Reconstruct”, “Understand” and “Localization” capabilities. This team is responsible for creating the high-fidelity visual and semantic maps—specifically textured, semantic meshes, and Gaussian Splats— as well as localization maps that allow our Large Geospatial Model (LGM) to perceive the world with human-like precision. Closely working with the R&D and product teams your work will bridge the gap between cutting-edge theory and real-world utility, turning complex geospatial data into a persistent sense of space for the next generation of AI and robotics. Job Responsibilities Applied Research & Implementation: Actively translate top-tier research papers (e.g., from CVPR, ECCV, NeurIPS) into production-grade features within our tech stack. Technical Leadership: Lead the design and implementation of 3D reconstruction pipelines, focusing on Structure from Motion (SfM) and high-fidelity mesh generation as well as 3D gaussian splats. Algorithm Optimization: Develop and optimize Gaussian Splatting quality algorithms and general ML code for high-performance execution on CPU and GPU. Production Implementation: Write and maintain high-performance, shader-based production code in C++ for Android and Linux environments. Technical Strategy & Mentorship: Work with engineering leadership to define the technical roadmap and quarterly objectives for the Applied CV Team; provide high-level mentorship and code governance to elevate the team’s technical bar. Cross-Functional Collaboration: Partner with the Research and Spatial Solutions teams to turn strategic goals into actionable plans. Quality & Benchmarking: Drive decision-making creating high quality data that allows the accurate spatial grounding of AI queries with structural, semantic and location specific knowledge. Job Requirements Years of Experience: 8+ years of professional experience in Computer Vision, Machine Learning, or a related field (or 6+ years with a PhD in a relevant domain). Education: Bachelor’s degree in Computer Science, Engineering, or a related technical field; Master's or PhD preferred. Core Technical Skills: Strong proficiency in C/C++ and Python for production-level software development. Specialized Expertise: Proven experience in 3D Computer Vision/ML, specifically with Structure from Motion (SfM), 3D reconstruction, and Gaussian Splatting rendering techniques. Hardware Optimization: Demonstrated ability to optimize algorithms for GPUs in Android or Linux environments. Graphics Knowledge: Experience with computer graphics and C++ shader-based implementations. Technical Leadership Experience: Previous experience tech leading a team of computer vision engineers in a high-growth environment. Work Location: This position requires 3 days per week in our San Francisco OR Sunnyvale office.

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. Join us as we build the real-world foundational models for physical AI, so people and machines can work efficiently and safely together. As a Senior Computer Vision Engineer on the Applied Algorithms team, you will own significant parts of our Localization technology. As a member of this team, you will be responsible for building the Map representing a living geospatial world model, as well as the centimeter-level accuracy Visual Positioning System (VPS) that allows people and robots to explore the real world. Part of the role will be bridging the gap between R&D and production, turning complex geospatial data into a persistent sense of space and enabling the next generation of spatial AI. Responsibilities System Architecture: Design, develop, and maintain production-grade computer vision systems that power Niantic Spatial’s VPS (Visual Positioning System) and 3D mapping pipelines. Algorithmic Innovation: Refine and scale algorithms for our VPS system including , SfM (Structure from Motion) and Feed Forwards models , efficient feature and descriptor extraction to have the best possible accuracy and recall. Moving algorithms from research concepts to hardened production code that can scale for our map of the world. Performance Engineering: Optimize complex ML and CV code for maximum efficiency in cloud and mobile environments, ensuring low latency and high-performance execution on GPU/CPU. Benchmarking & Evaluation: Create and own the tools and frameworks used to evaluate the quality of our spatial grounding and 3D maps against ground-truth data. Technical Leadership: Lead technical design reviews, mentor junior engineers, and serve as a team anchor for resolving complex technical disagreements within the mapping stack. Cross-Functional Delivery: Collaborate with Product, Research, and Operations teams to ensure that state-of-the-art computer vision solutions translate into delightful user experiences. Requirements Education: BS, MS, or PhD in Computer Science, Robotics, Computer Vision, or a related technical field (or equivalent professional experience). Years of Experience: 5+ years of experience developing and shipping algorithms for image processing, computer vision, or 3D reconstruction. Coding Proficiency: Expert-level proficiency in python and/or C++. Domain Expertise: Proven track record in designing solutions for Structure from Motion (SfM), VPS or 3D mapping.. Frameworks & Tools: Deep experience with Deep Learning frameworks (PyTorch or JAX) and version control (Git). Required in-office days: 3 days per week Plus If: Experience in planning and leading technical projects from inception to production. Significant contributions to open-source CV libraries (OpenCV, COLMAP, etc.). Experience with CUDA or shader programming for performance optimization. Candidate Privacy Policy 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 . If required by law, by submitting my job application I consent to the processing of my information as described in that Notice, including processing information I voluntarily disclose to Niantic Spatial, such as health or medical information, race or ethnicity data, and sexual orientation data and, in limited circumstances sharing information with third parties such as references and other third parties that assist in the hiring process. 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.

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. Join us as we build the real-world foundational models for physical AI, so people and machines can work efficiently and safely together. As a Senior Computer Vision Engineer on the Applied Algorithms team, you will own significant parts of our Localization technology. As a member of this team, you will be responsible for building the Map representing a living geospatial world model, as well as the centimeter-level accuracy Visual Positioning System (VPS) that allows people and robots to explore the real world. Part of the role will be bridging the gap between R&D and production, turning complex geospatial data into a persistent sense of space and enabling the next generation of spatial AI. Responsibilities System Architecture: Design, develop, and maintain production-grade computer vision systems that power Niantic Spatial’s VPS (Visual Positioning System) and 3D mapping pipelines. Algorithmic Innovation: Refine and scale algorithms for our VPS system including , SfM (Structure from Motion) and Feed Forwards models , efficient feature and descriptor extraction to have the best possible accuracy and recall. Moving algorithms from research concepts to hardened production code that can scale for our map of the world. Performance Engineering: Optimize complex ML and CV code for maximum efficiency in cloud and mobile environments, ensuring low latency and high-performance execution on GPU/CPU. Benchmarking & Evaluation: Create and own the tools and frameworks used to evaluate the quality of our spatial grounding and 3D maps against ground-truth data. Technical Leadership: Lead technical design reviews, mentor junior engineers, and serve as a team anchor for resolving complex technical disagreements within the mapping stack. Cross-Functional Delivery: Collaborate with Product, Research, and Operations teams to ensure that state-of-the-art computer vision solutions translate into delightful user experiences. Requirements Education: BS, MS, or PhD in Computer Science, Robotics, Computer Vision, or a related technical field (or equivalent professional experience). Years of Experience: 5+ years of experience developing and shipping algorithms for image processing, computer vision, or 3D reconstruction. Coding Proficiency: Expert-level proficiency in python and/or C++. Domain Expertise: Proven track record in designing solutions for Structure from Motion (SfM), VPS or 3D mapping.. Frameworks & Tools: Deep experience with Deep Learning frameworks (PyTorch or JAX) and version control (Git). Required in-office days: 3 days per week Plus If: Experience in planning and leading technical projects from inception to production. Significant contributions to open-source CV libraries (OpenCV, COLMAP, etc.). Experience with CUDA or shader programming for performance optimization. Candidate Privacy Policy 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 . If required by law, by submitting my job application I consent to the processing of my information as described in that Notice, including processing information I voluntarily disclose to Niantic Spatial, such as health or medical information, race or ethnicity data, and sexual orientation data and, in limited circumstances sharing information with third parties such as references and other third parties that assist in the hiring process. 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.