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# Senior Director of Product Management, Models and Data

- **Company:** Niantic, Inc.
- **Location:** London, United Kingdom
- **Experience:** Expert
- **Salary:** £77,464
- **Contract:** Permanent contract
- **Skills:** Training Data, Geographic Information Systems, Computer Vision, Machine Learning, Gaussian, Information Technology
- **Published:** November 6, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5955436841

## About the Role

* 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.

## Description

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.