---
title: 'AI Automation Engineer, Real-World Test Lab at Niantic Spatial'
canonical: 'https://feeny.ai/job/ai-automation-engineer-real-world-test-lab-niantic-spatial-san-francisco-dv63c1vyxy94'
type: 'job'
last_seen: '2026-10-19'
---

# AI Automation Engineer, Real-World Test Lab at Niantic Spatial

- **Company:** Niantic Spatial
- **Location:** San Francisco, CA
- **Compensation:** $158k–$210k
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-09-29
- **Last confirmed live:** 2026-10-19
- **Apply:** https://jobs.ashbyhq.com/niantic-spatial/1f60cda2-34e1-4b17-a17c-2489bc2c1788

## Job description

## 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 Real-World Test Lab

Physical AI doesn't get graded on a leaderboard. It gets graded on a factory floor at shift change, in a substation with no GPS, on a site where the lighting is wrong and the stakes are real. The Real-World Test Lab closes the gap between the benchmark and the field. We bring the customer's world inside our walls - their devices, their environments, their hardest conditions, and their definition of success - and make it the bar every release has to clear. As Niantic Spatial's first and most demanding customer, we push our reconstruction, localization, and spatial understanding to their limits, find where they shine and where they break, and turn that into evidence that shapes what we build next. It's a new team at the frontier of physical AI, and you'll help invent how the job is done.

## About the Role

We're hiring an AI Automation Engineer, reporting to the Director of the Real-World Test Lab, to build the system that produces our evidence. Today our evaluations are manual, inconsistent, and slow. You'll design and own the automation that runs them end to end on every relevant release, with no human driving it, turning one-off experiments into an always-on service the whole company relies on. This role is about owning the evidence, not executing a test plan. You'll decide how each workflow gets exercised, and you'll be measured on whether the company can trust the results, not on how much automation exists. The work counts when it keeps running correctly months later, without you in the loop. You believe evaluation is engineering, not process. You've built systems that test other systems, and you know the difference between a script that works on your laptop and infrastructure a team can trust. When a result couldn't be reproduced, you fixed the tooling instead of arguing about the number.

## What You'll Do

- Build the Evaluation Machine - Own the automation that executes Lab evaluations end to end: environment setup, run orchestration, artifact capture, and result collection. Make reruns free so we test constantly rather than occasionally.
- Make Results Comparable - Instrument scorecards so a result can be compared across product versions, devices, and capture conditions. A number without its lineage is not evidence.
- Automate the Agent-Driven Layer - Build the agent workflows that exercise priority customer use cases at realistic scale, across the graded difficulty spectrum from easy to frontier, and be honest about where agents cannot yet replace human judgment.
- Kill Manual Work Permanently - Convert one-off experiments into standing protocols that run on every relevant release. Automate recurring inspection wherever it can be automated, and route what genuinely cannot to scalable human review.
- Make Failures Actionable - Produce diagnostics precise enough that a finding reaches its owner with a reproducible case and data attached. Findings that need re-investigation before anyone can act on them are half-finished.
- Keep Data From Being the Bottleneck - Work with the AI Data Manager so every run is reproducible from a known dataset state, without anyone downloading and re-uploading data by hand.

What Success Looks Like

- 30 days: One priority workflow evaluated end to end with no manual steps, with results in a comparable scorecard.
- 60 days: Evaluations trigger automatically on relevant releases, with diagnostics that route failures to the right owners.
- 90 days: Three priority workflows under standing automated evaluation, with version-over-version comparison for leadership.

## What You'll Bring

- Built and maintained production-grade automation or test infrastructure that other engineers relied on daily.
- Experience evaluating systems where correctness is graded rather than binary, meaning quality, accuracy, or latency thresholds rather than pass/fail assertions.
- Strong Python, with fluency in orchestration, CI-style pipelines, cloud storage, and reproducible environments.
- Worked with backend services and APIs you did not own, integrating against them without becoming a bottleneck for their team.
- Written up a technical finding clearly enough that a non-author could act on it without a meeting.
- A bachelor's degree in a relevant field, or equivalent experience.

## Nice to Have

- Built agent-based or LLM-driven automation for a task that previously required human judgment.
- Worked on evaluation or benchmarking for computer vision, 3D reconstruction, or spatial systems.
- Instrumented dashboards or scorecards that leadership used for release decisions.
- Operated data provisioning or registry infrastructure across multiple environments and access models.

Competencies

- Intellectually honest. You design tests that produce answers people can trust, and you refuse to let a benchmark imply more than the evidence supports — including when the honest answer is inconvenient for your own work.
- Pragmatic. You find the fastest path to a defensible result without going through one-way doors that undermine scaling. You have no patience for building infrastructure nobody has asked for yet.
- AI-forward by instinct. You reach for agents and LLM-driven automation before you reach for headcount, and you're rigorous about verifying what they produce.
- Builds for other people. You've maintained something after the initial excitement wore off. Your tooling is documented, your failures are legible, and colleagues use what you build without asking you to run it for them.

## Compensation & Benefits

Base salary range of $158,400 to $210,000 per year. Compensation also includes an annual bonus, equity, and a comprehensive benefits package including medical, dental, and vision coverage, 401(k), and more.

Location & Work Model 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.

## 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.

## About Niantic Spatial

## Company Overview
- **One-liner**: Niantic Spatial builds real-world foundation models and spatial intelligence systems that enable machines to reconstruct, localize within, and understand the physical world at global scale.
- **Entity Type**: Private (startup, spun out of Niantic in 2025)
- **Headquarters**: San Francisco, CA, USA (with offices in Sunnyvale, Bellevue, London, and Tokyo)
- **Founded**: 2025 (as Niantic Spatial; roots trace back to Keyhole, founded 2001, and Niantic Labs, 2011)
- **Founders**: John Hanke (Founder & Executive Chairman); Thomas Gewecke (CEO), Brian McClendon (CTO), Inhi Cho Suh (President & COO)

## Core Business
- **Primary industry/industries**: Geospatial AI, physical AI infrastructure, spatial computing, robotics, defense, energy, construction, logistics
- **Target customers**: B2B – enterprises in robotics, oil & gas, defense, large venues, construction, logistics; public sector; also powers consumer apps via Scaniverse
- **Mission or purpose statement**: “Build a living model of the world that people and machines can talk to.”

## Products & Services
- **Large Geospatial Model (LGM)** – The core engine trained on one of the largest datasets of real‑world spatial imagery. It powers reconstruction, localization, and understanding capabilities at global scale. Accessed via Scaniverse and licensed to partners. (Model/API)
- **Reconstruct** – High‑fidelity, geo‑referenced 3D models (meshes and Gaussian splats) created from any standard camera, 360° camera, drone, LiDAR, or satellite imagery. (Technology/Service)
- **Localize (Visual Positioning System)** – Precise global positioning and orientation for humans and machines, even in GPS‑degraded or denied environments (indoors, outdoors, anywhere). (Technology/API)
- **Understand** – Spatial foundation models that give AI the ability to reason about the physical world: semantic understanding at every 3D point, queryable in natural language. (Technology/API)

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private company)
- **Key Metric**: Over 300 million user‑contributed scans; 30+ billion posed images from real‑world locations; 140+ patents filed or pending
- **Notable Investors/Partners**: Backed by Niantic (the Pokémon GO creator) and its internal resources; no external funding round publicly announced. Partnerships in defense, energy, construction, logistics, and large venues.
- **Growth Signals**: Formed as an independent company in 2025; actively hiring for senior engineering roles (Computer Vision, Tech Lead, AI Data Operations); expanding from consumer AR into enterprise and government sectors.

## Competitive Advantages
- **Deep heritage**: Founders built Keyhole (acquired by Google, became Google Earth/Maps) and later created the world’s first global Visual Positioning System at Niantic.
- **Unique data moat**: 300M+ crowdsourced scans and 30B+ posed images provide a proprietary dataset of real‑world spatial imagery that competitors would find extremely difficult to replicate.
- **Planetary‑scale deployment**: Proven ability to operate at global scale (Pokémon GO had 500M+ users); technology already battle‑tested in demanding consumer and enterprise environments.
- **Patent portfolio**: 140+ patents covering spatial reconstruction, localization, and understanding.

## Strategic Focus
- Continue building spatial foundation models that can reason about the physical world in natural language
- Expand vertical markets: robotics (warehouse, delivery, autonomous), defense (DDIL environments), oil & gas (facility digital twins), large venues, and construction
- Drive adoption of the Large Geospatial Model (LGM) as the standard for physical AI reasoning
- Integrate consumer‑facing Scaniverse as the front door to the LGM, simultaneously feeding the model with new user scans

## Why Work Here
- **Culture & mission**: High‑impact startup environment focused on a mission to create a living 3D model of the world; employees describe it as fast‑moving and collaborative
- **Work model**: Hybrid – blends in‑office collaboration with flexibility. Offices in San Francisco, Sunnyvale, Bellevue, London, and Tokyo.
- **Benefits**: 100% paid healthcare for employees, short‑/long‑term disability & life insurance (premiums covered), generous parental leave, flexible time off, 401(k) with company match, stock option grants (equity participation), and legal assistance (ARAG).
- **Engineering culture**: Deep technical challenges in computer vision, geospatial AI, large‑scale ML, and robotics; tight‑knit team of experts from Google Earth, Niantic, and the AR industry.
- **Growth trajectory**: Newly independent company with strong backing, ample opportunity to shape the product and technical direction.

## Sources
1. [nianticspatial.com – Homepage](https://www.nianticspatial.com/)
2. [nianticspatial.com – Careers](https://www.nianticspatial.com/careers)
3. [nianticspatial.com – About](https://www.nianticspatial.com/about)
4. [nianticspatial.com – About (alternate)](https://www.nianticspatial.com/en/about)

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