Software Engineer

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 will contribute by

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:

• The AI work is real, not aspirational. We self-host ML models we've built or fine-tuned, and our agent platform is in production today with real users. The architecture choices in this role (model serving, agent orchestration, context and memory management, eval infrastructure, GPU capacity) set the ceiling on how fast the rest of the company can move.
• Research and product sit close together. Our R&D group includes researchers building the models and systems behind the 3D digital twin. Your infrastructure is what turns that work into something users actually use, and you'll be in the room when those early architectural decisions get made - not handed a spec after the fact.
• The timing isn't incidental. Hover has product-market fit and the business is growing fast. Infrastructure decides whether we keep up, and this role grows with the company.

• 5-8+ years of professional software engineering experience, with meaningful time spent building infrastructure, platform, or distributed systems
• Strong software engineering fundamentals with production experience in Go, Python, or Ruby
• Experience building or working with systems on Kubernetes, beyond simply deploying applications
• Experience working with containerized systems such as Docker
• Experience building or using Infrastructure as Code tools such as Terraform (or similar tools like Ansible or Chef)
• Experience working with cloud platforms such as GCP or AWS, with willingness to learn or deepen expertise in GCP
• A systems-oriented mindset; you think about reliability, observability, failure modes, and scalability when designing systems
• Experience collaborating with engineers across teams to build platform capabilities or improve developer experience
• Comfort working across multiple layers of the infrastructure stack and learning new tools and technologies as the platform evolves

• Compensation - Competitive salary and meaningful equity in a fast-growing company
• Healthcare - Comprehensive medical, dental, and vision coverage for you and dependents
• Paid Time Off - Unlimited and flexible vacation policy
• Paid Family Leave - We support work/life balance and offer generous paid parental and new child bonding leave
• Mandatory Self-Care Days - A day set aside each month to allow employees to recharge
• Remote Wellbeing Resources - We provide recurring fitness classes, meditation/ mindfulness tools, virtual therapy, and family planning assistance
• Learning - We encourage continued education and will help cover the cost of management training, conferences, workshops, or certifications

Hybrid roles at Hover

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.

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. Why Hover wants you 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.