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About This Role
About Niantic Spatial
At Niantic Spatial, we're building the future of physical AI. Powered by a proprietary database of over 30 billion posed images, our groundbreaking mapping technology unlocks a new dimension of interaction and spatial intelligence that helps both humans and machines better understand, represent, navigate, and engage with the real environment.
Our reconstruction technology captures environments with geometric accuracy and extreme detail from any standard camera, and our Visual Positioning System delivers precise positioning almost anywhere in the world. We serve customers across robotics, the public sector, and energy and industrial markets - building for the 80% of economic activity that takes place beyond our screens.
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.
Compensation Range: $158.4K - $210K
Salary Context
This $158K-$210K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Niantic Spatial, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($184K) sits 13% below the category median. Disclosed range: $158K to $210K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Niantic Spatial AI Hiring
Niantic Spatial has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $210K - $300K.
Location Context
AI roles in San Francisco pay a median of $263,811 across 2,416 tracked positions. That's 24% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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