--- title: 'Software Engineering Intern (Summer 2027) at Niantic Spatial' canonical: 'https://feeny.ai/job/software-engineering-intern-summer-2027-niantic-spatial-san-francisco-hqycgn6h604f' type: 'job' last_seen: '2026-10-19' --- # Software Engineering Intern (Summer 2027) at Niantic Spatial - **Company:** Niantic Spatial - **Location:** San Francisco, CA - **Compensation:** $48–$70/hr - **Employment:** internship - **Work type:** onsite - **Posted:** 2026-10-06 - **Last confirmed live:** 2026-10-19 - **Apply:** https://jobs.ashbyhq.com/niantic-spatial/898b2da7-03cd-486e-96e3-3430a148c8fd ## Job 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 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 ## Company Overview - **One-liner**: Niantic Spatial builds real-world foundation models and spatial intelligence systems that enable machines to reconstruct, localize within, and understand the physical world at global scale. - **Entity Type**: Private (startup, spun out of Niantic in 2025) - **Headquarters**: San Francisco, CA, USA (with offices in Sunnyvale, Bellevue, London, and Tokyo) - **Founded**: 2025 (as Niantic Spatial; roots trace back to Keyhole, founded 2001, and Niantic Labs, 2011) - **Founders**: John Hanke (Founder & Executive Chairman); Thomas Gewecke (CEO), Brian McClendon (CTO), Inhi Cho Suh (President & COO) ## Core Business - **Primary industry/industries**: Geospatial AI, physical AI infrastructure, spatial computing, robotics, defense, energy, construction, logistics - **Target customers**: B2B – enterprises in robotics, oil & gas, defense, large venues, construction, logistics; public sector; also powers consumer apps via Scaniverse - **Mission or purpose statement**: “Build a living model of the world that people and machines can talk to.” ## Products & Services - **Large Geospatial Model (LGM)** – The core engine trained on one of the largest datasets of real‑world spatial imagery. It powers reconstruction, localization, and understanding capabilities at global scale. Accessed via Scaniverse and licensed to partners. (Model/API) - **Reconstruct** – High‑fidelity, geo‑referenced 3D models (meshes and Gaussian splats) created from any standard camera, 360° camera, drone, LiDAR, or satellite imagery. (Technology/Service) - **Localize (Visual Positioning System)** – Precise global positioning and orientation for humans and machines, even in GPS‑degraded or denied environments (indoors, outdoors, anywhere). (Technology/API) - **Understand** – Spatial foundation models that give AI the ability to reason about the physical world: semantic understanding at every 3D point, queryable in natural language. (Technology/API) ## Market Standing - **Valuation/Market Cap**: Not disclosed (private company) - **Key Metric**: Over 300 million user‑contributed scans; 30+ billion posed images from real‑world locations; 140+ patents filed or pending - **Notable Investors/Partners**: Backed by Niantic (the Pokémon GO creator) and its internal resources; no external funding round publicly announced. Partnerships in defense, energy, construction, logistics, and large venues. - **Growth Signals**: Formed as an independent company in 2025; actively hiring for senior engineering roles (Computer Vision, Tech Lead, AI Data Operations); expanding from consumer AR into enterprise and government sectors. ## Competitive Advantages - **Deep heritage**: Founders built Keyhole (acquired by Google, became Google Earth/Maps) and later created the world’s first global Visual Positioning System at Niantic. - **Unique data moat**: 300M+ crowdsourced scans and 30B+ posed images provide a proprietary dataset of real‑world spatial imagery that competitors would find extremely difficult to replicate. - **Planetary‑scale deployment**: Proven ability to operate at global scale (Pokémon GO had 500M+ users); technology already battle‑tested in demanding consumer and enterprise environments. - **Patent portfolio**: 140+ patents covering spatial reconstruction, localization, and understanding. ## Strategic Focus - Continue building spatial foundation models that can reason about the physical world in natural language - Expand vertical markets: robotics (warehouse, delivery, autonomous), defense (DDIL environments), oil & gas (facility digital twins), large venues, and construction - Drive adoption of the Large Geospatial Model (LGM) as the standard for physical AI reasoning - Integrate consumer‑facing Scaniverse as the front door to the LGM, simultaneously feeding the model with new user scans ## Why Work Here - **Culture & mission**: High‑impact startup environment focused on a mission to create a living 3D model of the world; employees describe it as fast‑moving and collaborative - **Work model**: Hybrid – blends in‑office collaboration with flexibility. Offices in San Francisco, Sunnyvale, Bellevue, London, and Tokyo. - **Benefits**: 100% paid healthcare for employees, short‑/long‑term disability & life insurance (premiums covered), generous parental leave, flexible time off, 401(k) with company match, stock option grants (equity participation), and legal assistance (ARAG). - **Engineering culture**: Deep technical challenges in computer vision, geospatial AI, large‑scale ML, and robotics; tight‑knit team of experts from Google Earth, Niantic, and the AR industry. - **Growth trajectory**: Newly independent company with strong backing, ample opportunity to shape the product and technical direction. ## Sources 1. [nianticspatial.com – Homepage](https://www.nianticspatial.com/) 2. [nianticspatial.com – Careers](https://www.nianticspatial.com/careers) 3. [nianticspatial.com – About](https://www.nianticspatial.com/about) 4. [nianticspatial.com – About (alternate)](https://www.nianticspatial.com/en/about) ## Other roles at Niantic Spatial - [3D Reconstruction Research Scientist/Engineer](https://feeny.ai/job/3d-reconstruction-research-scientist-engineer-niantic-spatial-london-jtj724025ekz) — London, United Kingdom - [AI Engineer, Computer Vision](https://feeny.ai/job/ai-engineer-computer-vision-niantic-spatial-san-francisco-z0gjw688cfax) — San Francisco, CA - [Staff Engineer - Robot Learning, Embodied AI](https://feeny.ai/job/staff-engineer-robot-learning-embodied-ai-niantic-spatial-san-francisco-4ef37kb9rjer) — San Francisco, CA - [Senior/Staff Product Engineer, Embodied AI](https://feeny.ai/job/senior-staff-product-engineer-embodied-ai-niantic-spatial-san-francisco-76wfnqgtf15f) — San Francisco, CA - [Hardware Operations Engineer - Robotics, Real-World Test Lab](https://feeny.ai/job/hardware-operations-engineer-robotics-real-world-test-lab-niantic-spatial-san-wda3z89zfvn3) — San Francisco, CA - [AI Automation Engineer, Real-World Test Lab](https://feeny.ai/job/ai-automation-engineer-real-world-test-lab-niantic-spatial-san-francisco-dv63c1vyxy94) — San Francisco, CA - [Senior Computer Vision Engineer, Embodied AI](https://feeny.ai/job/senior-computer-vision-engineer-embodied-ai-niantic-spatial-san-francisco-b1gqp4bj6mbr) — San Francisco, CA - [Research Scientist, Embodied AI](https://feeny.ai/job/research-scientist-embodied-ai-niantic-spatial-london-6pmyhj4kvh95) — London, United Kingdom - [GTM, Embodied AI](https://feeny.ai/job/gtm-embodied-ai-niantic-spatial-san-francisco-emef5va1xyyt) — San Francisco, CA - [Senior Product Marketing Manager, Embodied AI](https://feeny.ai/job/senior-product-marketing-manager-embodied-ai-niantic-spatial-san-francisco-8bymzx8g9pk9) — San Francisco, CA