Verkada Security Engineer Physical Ai Salary

Verkada Security Engineer Salary Overview (July 2026)

Verkada sits at the intersection of two hot 2026 hiring categories: physical security hardware (cameras, access control, environmental sensors) and applied AI (computer vision models running on-device and in the cloud for the “physical AI” features increasingly central to Verkada’s product pitch). As a late-stage private company most recently valued at approximately $4.7B in its 2024/2025 funding round, Verkada’s Security Engineer compensation as of July 2026 ranges from $160,000 at entry level to $460,000+ at Staff/Principal, with a comp structure that increasingly resembles an AI-infrastructure company’s rather than a traditional physical-security vendor’s, reflecting where the company’s actual hiring priority and budget now sit.

The “Security Engineer” title at Verkada spans two meaningfully different job families that candidates should not conflate: product/application security (securing Verkada’s own cloud infrastructure and camera firmware supply chain) and computer-vision/physical-AI security engineering (building the actual AI models that power features like person detection, license plate recognition, and anomaly detection). The latter category commands a substantial premium given the applied-AI talent market’s overall tightness in 2026.

Compensation by Level

Physical-AI/computer-vision-specific Security Engineer roles at L4 and above run 10-15% above the pure product/application security band, and this gap has widened, not narrowed, over the past 18 months as Verkada’s AI hiring competes directly against foundation-model labs for the same talent pool.

Why “Physical AI” Roles Are Verkada’s Highest-Comp Track

Verkada’s strategic bet through 2025-2026 is that physical security is being redefined by on-device and edge AI: cameras that don’t just record but actively interpret scenes in real time, with increasingly sophisticated anomaly detection and predictive alerting. This requires engineers with genuine computer vision and applied ML expertise — a talent pool Verkada competes for against companies like Scale AI, Waymo, and general foundation-model labs, not just against traditional physical-security competitors like Axis Communications or Genetec.

This cross-industry talent competition is the single biggest driver of Verkada’s recent comp growth in this specific track: candidates with computer vision research or applied ML backgrounds (even without prior physical-security industry experience) have reported offers 15-20% above what a candidate with a traditional security-engineering background but no CV experience would receive for a nominally similar title.

A second factor: Verkada’s on-device inference requirements (models must run efficiently on camera hardware with real power and compute constraints, not just in a data center) require a specialized “ML systems” skill set — optimizing models for edge deployment — that’s scarcer than general applied ML experience, further concentrating premium pay into a small subset of the Security Engineering org.

Interview Process

Verkada’s loop for Security Engineer roles (physical-AI track specifically) runs recruiter screen, a coding round, a computer vision/ML systems round (model architecture tradeoffs, on-device optimization, dataset and labeling pipeline design), a security-specific round covering threat modeling for camera/IoT firmware, a cross-functional round with hardware or product partners, and a final onsite. Candidates applying for the application/product security track (not physical AI) skip the CV/ML round in favor of a deeper cloud infrastructure security round instead.

Understanding which track you’re interviewing for matters enormously for negotiation — conflating the two during salary discussions is a common candidate mistake that leaves the AI-track premium unclaimed.

Negotiating a Verkada Offer

As with other late-stage private companies, treat Verkada’s option grants as optionality rather than guaranteed value, and anchor negotiation on base salary and, where relevant, sign-on bonus to bridge unvested equity left at a previous employer. The specific lever unique to Verkada: explicitly clarify and negotiate which track (application security vs. physical-AI/computer-vision) your offer reflects, since the bands differ by 10-15% and recruiters do not always volunteer this distinction upfront.

Candidates with computer vision or applied ML research backgrounds — even from adjacent industries like autonomous vehicles or robotics — have significant leverage to negotiate into the higher physical-AI band, since Verkada is actively trying to build out this specific talent pool and has documented flexibility to create custom offers for candidates who bring transferable CV expertise even without direct physical-security experience.

For a complete playbook on negotiating role-track ambiguity (getting clarity on which internal band applies to you before accepting a number), converting adjacent-industry expertise into leverage, and handling private-company equity uncertainty, The Big Tech Salary Negotiation Playbook (https://www.amazon.com/dp/B0DCQDB8HW?tag=sirjohnnymai-20) includes scripts directly applicable to hybrid hardware/AI security roles like those at Verkada.

2025-2026 Trends

Verkada’s headcount in its physical-AI and computer vision engineering functions has grown faster than any other part of the company through 2025-2026, reportedly outpacing overall company headcount growth by more than 2x, as the company continues repositioning its public narrative from “cloud-managed security cameras” toward “physical AI platform.” This repositioning has attracted competing interest (and competing offers) from both traditional physical security competitors and AI-native startups, intensifying the comp premium specifically for this track.

Frequently Asked Questions

Should I negotiate differently if I’m being considered for the physical-AI track versus the application security track? Yes — confirm which track applies to your offer before entering serious negotiation, since the physical-AI track carries a 10-15% premium at the same nominal level. If a recruiter is vague, ask directly which internal comp band the offer references.

How does Verkada’s Security Engineer comp compare to traditional physical security companies like Axis Communications or Genetec? Substantially higher, particularly at Senior level and above, reflecting Verkada’s cloud-native, AI-forward positioning versus the more traditional hardware-vendor comp models at legacy physical security companies.

Is prior computer vision experience mandatory to be considered for Verkada’s highest-paying Security Engineering roles? For the physical-AI track specifically, yes — direct computer vision, applied ML, or ML systems (edge deployment) experience is effectively required at Senior level and above. The application/product security track does not require this background and hires more broadly from general cloud security experience.