Senior/Staff Product Engineer, Embodied AI

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/Staff Product Engineer to join our Embodied AI team. Working alongside the group's GM, Product leader, and robot learning engineer, you'll own how the product reaches customers, build the integrations that make it native to their simulators and deployment stacks, and influence the roadmap.

You've turned capabilities into products. You've shipped developer-facing products and watched adoption turn on details nobody planned for. You know that a capability isn't a product until someone outside the building has used it without help, and you're passionate about the work that sits between the two.

This is not a role for someone who wants to manage and not ship, or someone who wants to ship without ownership of commercial consequences. This is a role for an entrepreneurial engineer who loves to build and is energized by making the people around them, researchers and customers alike, more effective. You have a co-founder mentality. You may have been part of a founding team before, or want to start your own business one day.

• Build the Product Surface – Own how autonomy teams experience our work: the APIs, UIs, packages, and documentation.
• Make Our World Native to the Customer's – Turn scans and reconstructions into reliable, interoperable assets that work in customers' simulation tools and workflows, including packaging, metadata, semantic labels, object structure, and selectable variants.
• Turn Prototypes Into Capability – Partner with reconstruction, mapping, localization, and research teams to transform prototypes and pipeline outputs into production-grade services and user experiences.
• Deliver at Scale – Design and operate backend systems for accounts, organizations, sites, scans, assets, processing jobs, permissions, asynchronous workflows, storage, and data access.
• Make It Work – Work directly with customers and internal users to integrate our technology, diagnose failures, improve reliability, and close the gap between intended and actual product behavior.
• Influence the Roadmap – Work alongside the group's GM, Product leader, and robot learning engineer to shape what we build and when.

What You'll Bring

• 6+ years of software engineering experience; Bachelor's degree in Computer Science or a related field, or equivalent practical experience.
• Significant experience building and operating production web applications and backend services, with strong fundamentals in API design, data modeling, testing, observability, and reliability.
• Shipped developer-facing products — including the documentation and reference code external engineers actually used — and worked with 3D asset formats or comparable binary formats to production depth: you can say what the conventions were, where they broke, and what it cost you.
• Proficient in Python and TypeScript, comfortable working across frontend and backend, and able to take a feature from product requirements through to running it in production.
• Experience designing asynchronous workflows, long-running jobs, external integrations, and data-intensive systems.

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.
• Confident and credible with customers. You can read an autonomy engineer's training config, find why our asset didn't load, and explain the fix in their terms.
• Fluent in other people's ecosystems. You'll spend much of your time learning new tools and stacks. You're quick to learn the customer's language, and you can point to something you shipped in a world that was foreign to you a quarter earlier.

• Experience working with robotics simulators or toolchains such as Isaac Sim, Isaac Lab, or MuJoCo, or OpenUSD.
• Built products for geospatial, 3D, computer vision, graphics, robotics, or spatial computing.
• Worked on visualization or asset-management experiences with React, Next.js, or Three.js.

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.

The base salary range for this role is $207,000 - $265,000, and your offer within it will reflect your experience and the scope you take on. Compensation also includes an annual bonus, equity, and a comprehensive benefits package including medical, dental, and vision coverage, 401(k), and more.

We are building for the >80% of economic activity that happens beyond the reach of screens. AI has learned from the text, images and video online, but it still lacks an accurate understanding of physical space.

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.