Software Engineering Intern (Summer 2027)

🏢 Niantic Spatial · 📍 San Francisco, CA · 💼 onsite · ⏰ Intern

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Position Summary & Responsibilities

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

🎯 Candidate Preparation Guide For Niantic Spatial Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Assessing computer vision expertise, 3D spatial mapping, and AR rendering performance constraints.

📋 Preparation Priorities (Key Areas to Clarify):

1. Relying on heavy, unoptimized machine learning models that drain mobile battery instantly
2. Treating 3D coordinate spaces theoretically without practical rendering awareness
3. Failing to account for variable lighting, occlusion, and GPS drift in outdoor AR

✨ Recommended Role Alignment Hook:

I bridge computer vision and spatial computing to build low-latency AR experiences that anchor seamlessly to the physical world.

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