Software Engineering Intern (Summer 2027)

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.