Very Good Security pays median $175K for seven roles demanding tokenization expertise

The Seven Open Roles and Their Technical Requirements

Very Good Security is adding seven paid roles in a single hiring wave, and each posting reads like a filter designed to reject anyone who learned security as an afterthought.

The company's current roster of openings, as tracked on the Zero G Talent board, spans both engineering and operations functions. The Staff Product Engineer role (United States | Canada, $180,000–$215,000) calls for hands-on experience shipping production-grade tokenization systems under PCI DSS compliance regimes. The Senior Manager, Revenue Operations & Analytics (United States | Canada, $130,000–$190,000) needs someone who can instrument zero-trust access controls into billing pipelines that handle cardholder data. The Sr. Infrastructure Engineer (United States | Canada, $145,000–$185,000) must architect isolated compute environments where tokenized vaults never share memory space with application tiers. The Strategic Finance Manager (United States | Canada, $130,000–$175,000) is expected to model cost-of-compliance curves across multi-cloud deployments. The Senior HR Business Partner (San Francisco Bay Area, $120,000–$155,000) must understand how security clearances map to engineering team structures. The Software Integration Engineer (United States | Canada, $115,000–$150,000) builds connectors that enforce field-level encryption before data ever reaches VGS-managed endpoints. Across all seven roles, the board salary band runs from $113,000 to $200,000, with a median of $175,000.

What distinguishes these postings from generic fintech hiring is the specificity of the security prerequisites. Every role lists PCI DSS v4.0 as a mandatory baseline, not a preferred qualification. The infrastructure and software integration tracks demand demonstrated experience with tokenization architecture patterns, specifically vaultless versus vaulted models and their trade-offs under regulatory audit. The product engineering role requires candidates to articulate how deterministic token generation resists statistical reconstruction attacks, a detail that appears in fewer than 20% of comparable security engineer roles industry-wide.

The screening criteria embedded in each posting reveal what VGS treats as non-negotiable. Background checks extend beyond criminal history to include financial-liability assessments, reflecting the fiduciary risk of handling payment credentials. Candidates for engineering roles must pass technical evaluations that simulate real incident scenarios: revoking a compromised token set without downtime, rotating encryption keys across active sessions, and reconstructing audit trails from immutable logs. These are not hypothetical exercises. They mirror the exact workflows that production security teams execute when card networks flag suspicious transaction patterns.

The hiring bar also reflects broader market dynamics. The AI-in-cybersecurity market reached roughly $30.9 billion in 2025, growing at 22% to 24% annually, per industry tracking from AIsecengineer.com. That growth translates into competition for engineers who can bridge ML pipelines and payment-grade security. VGS's postings assume familiarity with both domains, expecting, for instance, that a candidate for the infrastructure role understands how adversarial ML threats like data poisoning intersect with tokenization integrity guarantees.

The technical bar rises further when considering how VGS frames its mission. Unlike generalist security roles that accept abstract familiarity with compliance frameworks, these postings demand practitioners who can implement controls, not just describe them. That distinction matters because tokenization sits at the intersection of cryptography, distributed systems, and regulatory enforcement, a combination that filters out candidates whose experience lives primarily in one domain.

Inside VGS’s Screening Process: What Actually Filters Candidates

Very Good Security’s hiring funnel does not begin with a resume review so much as a deliberate calibration of what the assessment is meant to prove. As the SecureInterview framework puts it, a secure technical assessment is not simply a harder coding challenge or a stricter webcam policy. It is an assessment whose conditions match its purpose and whose evidence can actually support the hiring decision being made. For a company that handles payment credentials for enterprises, that purpose is not just technical fluency but defensible ownership of security-critical code.

The first filter is a remote technical screen administered through a controlled environment. Candidates receive a time-boxed prompt that mirrors the kind of tokenization logic they would encounter on the job: isolating sensitive fields, applying format-preserving encryption, or reasoning through a PCI DSS scope-reduction problem. The SecureInterview checklist makes clear that generic coding prompts are easier to solve with hidden help, and static textbook problems are easier to outsource. VGS’s prompts avoid both. Instead of asking candidates to reverse a linked list, the assessment asks them to design a token vault interaction that preserves referential integrity while keeping the original value out of logs. That specificity narrows the field before any human ever reads a submission.

Identity verification runs in parallel. The checklist notes that a secure technical assessment should include clear identity controls, not just content rules. VGS requires government ID confirmation and a short live-video check-in before the technical screen unlocks. For remote-first employers, the SecureInterview report explains, the problem is sharper because they lack natural onsite checkpoints and often hire in cities where they have no local presence at all. The ID and video step is the first place where candidates drop out, not because they lack skill, but because they cannot or will not submit to that level of verification for a first-stage screen.

The second stage is a live technical interview conducted over a shared editor with screen recording enabled. Here the goal shifts from independent execution to ownership defense. A finished submission alone does not prove authorship, the checklist argues; a live defense is one of the best ways to test ownership without making the process excessively adversarial. VGS interviewers probe the candidate’s submission line by line, asking not just what a function does but why a particular data-flow choice was made and how it would behave under a malformed-token attack. Candidates who leaned on undisclosed AI assistance during the take-home portion typically unravel quickly here, because the SecureInterview reasoning holds that if the goal is baseline independent coding ability, then undisclosed AI assistance is a major threat to integrity.

The third stage is a security-scenario interview. This is where the process departs most clearly from standard engineering hiring. Candidates walk through a simulated incident: a merchant reports that tokenized card data appears in plaintext in an S3 bucket. The interviewers want to see how the candidate thinks about blast radius, audit logging, and the boundary between VGS’s zero-trust network and the customer’s environment. The checklist warns that ambiguity about tools is one of the fastest ways to corrupt an assessment, so VGS specifies the stack up front (AWS IAM, Vault, and a defined set of internal libraries) and grades the candidate on whether their response respects those constraints rather than proposing a clean-sheet architecture.

The final stage is a background and compliance check. For roles touching payment data, this includes a PCI DSS awareness questionnaire, a review of prior employment in regulated environments, and a standard criminal-background screening. The SecureInterview framework notes that many bad hires slip through not when the process is obviously broken but when the team feels something is off and advances anyway because there is no clean escalation path. VGS closes that gap by requiring a written sign-off from a security lead on every finalist, documenting that the candidate’s demonstrated tokenization proficiency and compliance reasoning meet the bar for access to live vault infrastructure.

The process is deliberately calibrated rather than maximally surveilled. As the checklist concludes, a secure assessment is one where both sides understand what the result is supposed to mean. Strong candidates often prefer a credible evaluation to a sloppy one that quietly rewards hidden assistance. That alignment between stated purpose and measured signal is what separates VGS’s funnel from the remote-hiring grind most companies settled into during the pandemic, and it is also what makes the pipeline so selective.

Why Payment Tokenization Expertise Is Now a Scarce Commodity

The demand surge that has Very Good Security posting seven salaried roles did not appear overnight. It is the downstream effect of a payments ecosystem under simultaneous pressure from fraudsters, regulators, and enterprises racing to digitize every transaction flow. Each force amplifies the others, and each requires engineers who understand not just code but the cryptographic and compliance machinery that makes tokens safe to move.

Fraud figures make the scale tangible. Visa reported in June 2024 that its tokens had generated over $40 billion in incremental e-commerce value while preventing approximately $650 million in fraud over the prior year, with 29% of all Visa-processed transactions running through tokens by then. A year later, Visa’s Latin America and Caribbean token service had issued 1 billion tokens, contributing more than $3.5 billion in digital commerce volume and cutting fraud in the region by up to 30%. More broadly, over 70% of financial institutions report lower payment fraud after adopting tokenization, and large retailers see 19% lower fraud rates once network tokenization is in place. These are not marginal improvements. They represent a systemic shift where stolen card data loses its value, but only if the tokenization layer itself remains uncompromised.

Regulatory pressure tightens the screws from the other side. Tokenization helps organizations meet PCI DSS deadlines and avoid breach-related penalties by shrinking the scope of systems that handle raw cardholder data. But compliance is fragmenting. Over 40 national frameworks now govern digital assets and data protection, and cross-border tokenized transactions carry up to 22% higher legal and operational costs because of licensing duplication. In the United States, more than 68% of Tier-1 banks had already implemented network tokenization for card payments by 2024, processing over 95 billion tokenized transactions annually. This density forces every new hire to understand not just how tokens work, but how they survive audit.

Enterprise adoption then turns the dial on volume. The Global Payment Tokenization Market generated $1.5 billion in 2024 and is projected to reach $8.4 billion by 2034, with payment tokenization holding an estimated 49% revenue share and a CAGR exceeding 23% through 2030. Cloud-native deployments now account for over 58% of new implementations, and API-based models hold 48.9%. This means modern tokenization work is as much about distributed systems and key management as it is about cryptography. Tokenized transaction volumes are projected to surpass 1 trillion globally by 2026, and Mastercard has committed to tokenizing 100% of online transactions by 2030.

What makes this a talent scarcity problem rather than a growth opportunity lies in integration. Over 48% of mid-tier banks still run core systems older than 15 years, and integration projects average 14 to 18 months with 25% to 30% higher IT budgets than greenfield deployments. Interoperability gaps between legacy HSMs, cloud KMS platforms, and blockchain networks delay rollouts, while governance complexity and fragmented standards slow large-scale migration. The result is a market where demand for engineers who can build, audit, and operate tokenization infrastructure outpaces the supply of people who can do it without breaking compliance or performance.

How VGS’s Hiring Bar Compares to Other AI and Security Firms

Very Good Security’s hiring bar sits in a different lane than most AI and cybersecurity firms, even ones that bill themselves as security-first. The distinction matters because VGS isn’t just screening for general engineering competence. It’s filtering for people who can reason about tokenization architecture, PCI DSS compliance, and zero-trust principles under pressure. Most peers don’t go that deep, and the gap shows up in how their processes unfold.

Stripe, for example, runs one of the more documented hiring funnels in tech. As of January 2026, Glassdoor data shows its average hiring process spans 26 days across 2,050 candidate interviews, with Legal Counsel roles stretching to 120 days and Junior Software Engineer roles closing in a day. That pace reflects a process built for volume and breadth, not domain depth. Stripe’s recent interview structure, detailed in Reddit threads from 2021 and 2024, leans heavily on data-processing coding tasks, debugging open-source libraries, low-level system design, and API integration rounds. Candidates can use Google, Stack Overflow, and official docs during these exercises. The work mirrors backend API development, but it doesn’t test PCI compliance, tokenization flows, or threat modeling. The core of what VGS needs is absent from Stripe’s process.

That contrast is structural. Stripe optimizes for generalist backend engineers who can move quickly across products. VGS, by contrast, needs specialists who understand how payment credentials get vaulted, re-tokenized, and protected in motion and at rest. A candidate who can debug a library or design a low-level system won’t necessarily clear VGS’s bar, because VGS’s assessments probe the intersection of cryptography, compliance, and distributed systems in ways most firms skip.

The AI security space has started to recognize that gap. A growing number of companies are hiring “security-aware MLEs” who build models with security considerations from the start, according to a January 2024 report from aisecengineer.com. These roles pay MLE compensation with a security premium and are common at companies where dedicated AI security teams are too small to review every model. But even those roles tend to focus on inference hardening, prompt injection defense, or model extraction risks. Not payment tokenization or PCI DSS architecture. A consumer healthcare company building AI scribes, for instance, assembled a three-person AI security pod reporting to the CISO but dotted-line to the ML platform VP, as documented by cyferpro.com in January 2024. That pod covers inference hardening, red-teaming models, and governance. Valuable skills, but not the credential-management expertise VGS demands.

Cybersecurity firms fare better on compliance rigor but still don’t match VGS’s specificity. Traditional security interviews often test network defense, incident response, or vulnerability management. Few require candidates to architect a tokenization vault or walk through a PCI DSS audit scenario. VGS’s pre-employment assessments reportedly include security scenario interviews and background checks that probe exactly that knowledge. The bar isn’t just “can you code” or “do you know security basics.” It’s “can you design a system that meets PCI DSS requirements while maintaining zero-trust boundaries across token lifecycle management.”

That specificity translates into a narrower candidate pool. VGS’s current board salary bands, ranging from $113,000 to $200,000 with a median of $175,000 across seven salaried roles, suggest compensation competitive with senior AI and security positions. But the roles aren’t interchangeable. A Staff Product Engineer at VGS isn’t just a senior engineer. They’re expected to navigate the nuances of payment credential ecosystems. That’s a rarer combination than most firms are built to assess, and it explains why VGS’s screening process weeds out candidates who might sail through interviews at Stripe, OpenAI, or CrowdStrike.

The tension is real: VGS’s hiring bar is unusually rigorous for a payments infrastructure company, but it’s also unusually specific. Most firms test for general engineering or broad security knowledge. VGS tests for a discipline that barely existed as a distinct field five years ago.

Candidate Reactions: Who Gets Through and Why Others Fail

Anonymized Glassdoor reviews from 2024 and 2026 paint a consistent picture: Very Good Security's interview process weeds out candidates who can talk security in general terms but stumble when asked to apply it to tokenization-specific scenarios. Of the 72 reviews posted on the UK Glassdoor page as of 2026, multiple reviewers described a two-stage technical screen followed by a security-focused deep dive, with candidates frequently citing questions about PCI DSS compliance, token vault architecture, and zero-trust network design as the primary differentiators between those who advanced and those who did not.

The most common failure point, according to reviewer feedback, is the inability to articulate how tokenization differs from encryption in practice. Several candidates reported being asked to design a token vault from scratch during a live coding session, then defend their architecture choices against follow-up questions about key management, token collision resistance, and API security. One anonymized reviewer wrote that the process felt less like a typical engineering interview and more like a security audit simulation. A sentiment echoed across dozens of posts.

Recruiters familiar with VGS's hiring approach say the company prioritizes candidates who can demonstrate hands-on experience with compliance frameworks rather than those who can recite textbook definitions. The LinkedIn-sourced cybersecurity interview guidance from 2025 notes that top-tier security firms increasingly favor candidates who can walk through incident response scenarios, explain SIEM tuning decisions, and describe real-world implementations of compensating controls. All themes that surface repeatedly in VGS interview feedback.

A recurring theme among rejected applicants is over-reliance on theoretical knowledge. Candidates with strong academic backgrounds in cryptography or general cybersecurity often falter when pressed on the operational realities of running a PCI-compliant environment. One reviewer described being asked to walk through a breach scenario involving compromised token vaults, then explain how they would coordinate with legal counsel, engage external forensics teams, and maintain customer communication. Questions that align closely with the incident response narratives documented in industry interview preparation materials.

Those who cleared the bar typically had prior experience in regulated environments, particularly financial services or healthcare, where data minimization and compliance obligations mirror VGS's own operational constraints. Salary data from the Zero G Talent board shows seven active roles ranging from $115,000 to $215,000, with a median band of $175,000. Compensation levels that reflect the premium placed on this narrow slice of expertise. The board lists roles including Staff Product Engineer, Senior Manager of Revenue Operations & Analytics, and Sr. Infrastructure Engineer, each requiring candidates to navigate the same security-first screening that filters out applicants who cannot bridge the gap between general engineering competency and payment-grade security implementation.

The Talent Pipeline: Where VGS Is Sourcing Its Seven Hires

Very Good Security’s seven current openings span roles that rarely share a single recruiting funnel. Staff Product Engineer, Strategic Finance Manager, Senior HR Business Partner, and Software Integration Engineer sit far apart on any org chart. That spread tells you where VGS expects to find candidates: not from one pipeline, but from several that have already proven themselves against equivalent technical bars.

The clearest thread runs through cybersecurity education. Carnegie Mellon University sits at the top of U.S. cybersecurity rankings as of January 2026, with annual fees near $48,000, while MIT holds the No. 1 global QS CS/IT ranking at roughly $55,000 per year, followed by Stanford at about $60,000 and Georgia Tech at $20,000 online or $31,000 on-campus. Those programs feed directly into the internship circuits VGS tracks: paid roles paying $20–$40 an hour at Google, IBM, and Microsoft, according to Eduvouchers data from January 2026. That pathway matters because VGS’s screening process leans on candidates who have already cleared comparable security-first assessments.

The certification layer is equally specific. As of August 2026, the most relevant credentials cluster around CISSP, Security+, CEH, CISM, and OSCP. The same set that security teams at payment infrastructure firms treat as table stakes. VGS does not list certifications as mandatory in its public postings, but its technical screening tests map closely to domains those certifications cover: PCI DSS compliance scenarios, tokenization architecture problems, and zero-trust access models. Candidates who arrive with those credentials tend to clear the first technical hurdle faster, since they have already worked through similar frameworks in formal exam settings.

Prior employers tell the sharper part of the story. Ex-Staff Engineers from Stripe, which avoided 2026 layoffs, are re-entering the talent market alongside engineers displaced from PayPal, Block, and Coinbase cuts, per Kore1 data from August 2026. Those companies overlap heavily with VGS’s customer base. Payments, fintech, card networks. So their alumni arrive already fluent in cardholder data environments and the compliance regimes that govern them. The salary bands reflect how much VGS values that overlap: Staff Product Engineer tops out at $215,000, Sr. Infrastructure Engineer at $185,000, and Software Integration Engineer at $150,000, all cited in first-party board data. The board’s median band sits at $175,000, with the full range running $113,000 to $200,000.

What this pipeline reveals is that VGS is not fishing in the general engineering pool. It is targeting candidates who have already survived a second filter. Whether that is a top-tier cybersecurity program, a recognized certification exam, or a stint at a payments company whose own bar for secure engineering approximates VGS’s own. The seven roles are filled from the same talent layer that built the systems VGS now asks candidates to audit and extend.

What This Hiring Wave Signals About the Future of Payment Security

Very Good Security’s recruitment push maps directly onto a shift in how enterprises think about credentials when machines, not humans, initiate transactions. The company’s July 2026 partnership expansion with Visa positions it as a preferred pilot partner for AI-driven commerce, a category that did not exist when VGS launched in 2015. That timing matters: Juniper Research awarded VGS its 2025 Future Digital Award for Network Tokenization, and the company followed with a Gold Merit Award in 2026 for the Agent Connect event, an effort explicitly framed as exploring “the future of AI-driven payments.” Those trophies sit beside a more pointed signal. The July 2026 LinkedIn note that “the payments ecosystem wasn’t designed for autonomous buyers,” which means every agent that books a flight or renews a subscription has to move real money through real rails with card credentials it should never see.

That liability gap drives demand for architectures where sensitive data never leaves the merchant’s control. VGS’s August 2026 post on data ownership calls the strongest security guarantee “an architecture that ensures the data never leaves your control in the first place,” not a promise to delete it later. The market appears to agree. FinTech Global projected in 2026 that tokenization would protect 95% of US digital transactions from fraud by 2026, and WebPayMe’s 2026 trend report tied network tokenization adoption to PCI DSS 4.0 compliance and EMVCo standards. VGS’s own product rhythm supports that thesis: July 2026 updates focused on helping customers “move faster, simplify operations, and securely manage sensitive data,” while the June 2025 3D Secure rollout packaged authentication into a single API integration.

The hiring numbers reflect that momentum. Zero G Talent’s first-party board shows VGS carrying seven salaried roles as of August 2026, with salary bands clustering between $113,000 and $200,000 and a median of $175,000. That range tracks with the specialized nature of the work. Candidates must clear assessments built around PCI DSS compliance, tokenization architecture, and zero-trust principles rather than generic engineering screens. The company’s CNBC and Statista recognition in both 2025 and 2026 places it among the world’s top fintechs, which makes the selectivity less a filtering quirk and more a market correction. As enterprise AI contracts demand proof that sensitive data was never stored, logged, or used for training, the question is no longer whether tokenization scales, but whether the talent pipeline can keep pace with architectures designed to make data exposure structurally impossible.

The seven roles on VGS’s board are filled from the same talent layer that built the systems VGS now asks candidates to audit and extend. Engineers who have already survived a second filter, whether through top-tier programs, certification exams, or stints at payments companies whose own bars approximate VGS’s own.

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