Senior Director of Product Management, Models and Data

AI has learned from almost everything online, but it still struggles with the physical world, where 99.5% of economic activity happens. It can't yet reason about real places well enough for robots, field crews and AI agents to rely on it, and closing that gap will change how factories, power grids, cities and defense operations run.

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.

The Large Geospatial Model is Niantic Spatial's foundation model of the physical world. It learns from years of real-world captures of millions of places, and people, robots and AI agents use it to find out where they are, what is around them and what has changed. Each step up in scale has given the model capabilities it did not have before. The next problems are some of the hardest in AI: filling in the parts of a place no camera saw while marking what was inferred, measuring what changed between visits, and predicting what will change next.

Customers already use the model in production. Energy companies use it to guide field crews to equipment on large industrial sites, defense partners use it for positioning where GPS is jammed or unavailable, and robotics companies train robots in metric-scale copies of the real sites they will work in.

You will be the first product leader for the model and the data it learns from, reporting to the Chief Product Officer. You decide which capabilities it gains and in what order, the bar each release has to clear, and what data Niantic Spatial collects, licenses or partners for to get there. You set those priorities directly with the Chief Scientist and the London research team who train the model.

If you have shipped a frontier language model, most of the job will be familiar: evals, data mix and release decisions. The job differs in two ways. Every output of this model can be scored against a measured real place, so its evals rest on ground truth. And its data is collected in the physical world, so choosing what to capture next is a product decision with a real cost. Experience in 3D or mapping is welcome but not required.

By the end of your first year, research, engineering and the product leads for industrial, defense, robotics and platform customers work from one model roadmap that you own, each release ships against evals you wrote, and the model's newest capabilities are in customers' hands.

• Own the Model Roadmap - Decide the order in which the Large Geospatial Model gains its next capabilities, from filling in unseen parts of a site to measuring and predicting change, based on what industrial, defense, robotics and platform customers will pay for. Turn customer problems into specific research requirements, and bring new research results to the product leads who sell into each market.

• Set the Release Bar - Define the evals and acceptance criteria for each capability with the Real-World Test Lab, which measures model output against ground truth in real places. Decide whether each release ships or goes back for more data or training.

• Own the Data Plan - Decide what data Niantic Spatial collects, buys, licenses or gets through partners, and set the product terms of those deals with Legal and Business Development. Set the curation, labeling, consent, privacy and training-rights requirements every dataset has to meet, and keep a record of which data went into each model version.

• Turn Capabilities into Products - Decide when a new model capability is ready to become something customers buy, what it promises them, and how it reaches them through Niantic Spatial's products and platform.

• Set the Long-Term Direction - Decide where the model goes over the next several years, as it moves from describing places as they are to tracking how they change and anticipating what comes next, and make the case for the research and data investment that takes.

• 8+ years in product management, including at least three years shipping a large model to users or customers.

• Experience writing evals for a model and using them to decide when a release was ready.

• Direct work on training data, such as setting a data mix, running labeling or human-feedback programs, or negotiating data licenses.

• Enough depth in how large models are trained, post-trained and evaluated that researchers bring you into their technical decisions. Many strong candidates for this role were ML engineers or researchers before they moved into product.

• Experience setting direction for research and engineering teams that did not report to you.

• A bachelor's degree in computer science, engineering, or a related field, or equivalent experience.

• Experience in 3D computer vision, reconstruction, localization, mapping, geospatial data, robotics or autonomous systems.

• An advanced degree in machine learning, computer vision, robotics, or a related field.

• Experience with Gaussian splatting, neural fields or visual positioning systems.

• Experience with government or defense requirements for data provenance and security.

Base salary: Competitive market rate. Compensation also includes an annual bonus, equity, and a comprehensive benefits package.

This role is based in our London office, with three days per week in office.

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.

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.

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