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San Francisco, California, United States
Hybrid
Hover helps people design, improve, and protect the properties they love. With proprietary AI built on over a decade of real property data, Hover answers age-old questions like “What will it look like?” and “What will it cost?” Homeowners, contractors, and insurance professionals rely on Hover to get fully measured, accurate, and interactive 3D models of any property — all from a smartphone scan in minutes.
At Hover, we’re driven by curiosity, purpose, and a shared commitment to serving our customers, communities, and each other. We believe the best ideas come from diverse perspectives and are proud to cultivate an inclusive, high-performance culture that inspires growth, accountability, and excellence. Backed by leading investors like Google Ventures and Menlo Ventures, and trusted by industry leaders including Travelers, State Farm, and Nationwide — we’re redefining how people understand and interact with their spaces.
You’ve built and scaled infrastructure platforms before, and you want the next one to be yours at a company where the platform itself is evolving because of what’s running on top of it. Modern AI workloads place fundamentally different demands on infrastructure than traditional services do, from LLM inference and agent runtimes to vector stores, GPU economics, and real-time 3D and computer vision pipelines. You understand these workloads, what they require, what breaks first, and how quickly costs can spiral without the right abstractions in place. You see infrastructure as the layer that determines whether an AI strategy ships or stalls out. At the same time, you judge a platform by whether the engineers using it move faster, favor simple solutions over clever ones, and stay close enough to the code to lead through both architecture reviews and pull requests.
The Infrastructure team builds the platform under Hover's application layer. The rest of engineering is our customer, and our job is to make the fastest path also the most reliable and secure path. That covers our Kubernetes platform, cloud infrastructure, deployment workflows, and geospatial pipelines. It also increasingly covers the AI infrastructure behind Hover's agent platform and 3D digital twin: model serving, agent orchestration, eval pipelines, retrieval systems, and the data plumbing that feeds them.
As more of the product runs on AI, this team's scope is widening and the work is getting harder. This isn't a DevOps or SRE role. Infrastructure here is a software engineering problem. We build systems and tooling that eliminate manual work, and we hold ourselves to the same engineering bar as any product team. This role is the senior technical partner to the Lead of Systems Engineering. You'll shape the architecture, push the team's standards, and own outcomes the business depends on.
You'll help define and build the platform every other engineer at Hover relies on. That means setting technical direction on the systems that matter most, working closely with backend, frontend, ML, CV, data, and security teams to get things into production, and mentoring engineers as the team grows.
Concretely: you'll standardize how services run, improve deployment workflows, build tooling, harden reliability and observability, and keep cost in check as ML and 3D workloads scale. The job is building systems, not operating them by hand. The output is engineers across Hover shipping faster.
A few things that make it worth your attention:
Hover has Hubs in San Francisco and New York City, where we expect that all employees living within a 50-mile radius of our offices will come into their local Hover office at least three times a week to build rapport and foster organic connection. At this time, Hover is not considering fully remote roles.
The US base salary range for this full-time position is $194,000 - $239,000 annually. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all applicable US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
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