The 18 Roles Cantina Is Filling
Cantina has opened 18 new roles across AI, engineering, and product in recent weeks — a hiring surge that coincides with a deliberate tightening of its screening process to prioritize demonstrable AI project work and cultural alignment. Zero G Talent's live board currently tracks 16 salaried Cantina listings, with one new role added in the past seven days: a Machine Learning Engineer, Images position based in the Bay Area or available remotely, at $200,000–$265,000 per year. That cadence (a fresh posting landing within a week of the broader surge) points to a rolling intake, not a one-time batch fill.
The board's current Cantina listings break into three categories. On the AI side, a Member of Technical Staff focused on Data & ML Infrastructure for Video Models is remote and open to candidates in the U.S. or Europe, with a band of $200,000–$260,000 per year. Product and marketing roles include a Product Director for Video Products in California ($200,000–$260,000) and a Director of Brand Marketing in Los Angeles ($160,000–$240,000). Engineering roles round out the spread: a Staff Software Engineer for Backend work in Los Angeles or San Francisco ($200,000–$240,000) and an Android Engineer at the Senior to Staff level, also in LA or SF ($180,000–$240,000). These titles, spanning machine learning infrastructure, video product leadership, brand marketing, backend systems, and mobile, show the push cuts across more than one discipline.
| Category | Role | Location | Salary Band |
|---|---|---|---|
| AI | Member of Technical Staff, Data & ML Infrastructure for Video Models | Remote (U.S./Europe) | $200,000–$260,000 |
| Product | Product Director, Video Products | California | $200,000–$260,000 |
| Product | Director of Brand Marketing | Los Angeles | $160,000–$240,000 |
| Engineering | Staff Software Engineer, Backend | Los Angeles or San Francisco | $200,000–$240,000 |
| Engineering | Android Engineer (Senior to Staff) | LA or SF | $180,000–$240,000 |
The board's salary data places the typical band at $125,000–$260,000, with a median of $225,000. Several current postings sit at the top of that range (the Machine Learning Engineer and Product Director roles both reach $265,000 and $260,000 respectively), which tells you Cantina is competing for senior and staff-level talent, not filling junior seats. Candidates who bring the experience those bands assume matter more than entry-level promise. The combination of role breadth, seniority level, and the rolling cadence of new postings points to a company in an acceleration phase. Cantina is building out capacity across the AI and product layers simultaneously — adding the ML infrastructure to support real-time video generation and the product leadership to guide it to market. The hiring push cuts across the technical and product organizations, which means the emphasis on shipped project evidence and how candidates connect with a team applies to a wide cross-section of candidates, not just a narrow slice. For job-seekers tracking this story, the takeaway is straightforward: Cantina is in a mode of expansion that spans disciplines and seniority bands, and the surge represents a deliberate bet on the company's near-term trajectory, not a reaction to a single vacancy.
A Screen That Rewards Personality Over Pedigree
Cantina builds social AI characters that talk, perform, and interact with anyone. That product reality (real conversations, not research papers) shapes how the company screens candidates for its current openings. The process weighs shipped project work and cultural alignment above formal credentials, a combination that sets Cantina apart from the credential-first filters still common at larger incumbents.
The hiring philosophy, per a PropTech Founder's widely shared interview-prep video, centers on a blunt premise: "there's always going to be someone better at ML and AI than you are, and coding in general." That statement reframes the screen around something harder to teach than technical chops — personality and the ability to connect with a team. "The candidate that makes the interviewer think more deeply. The candidate that sparks an emotional response out of the interviewer" — this is the bar Cantina's screen raises, the same source said. The creator called it "the most interesting candidate" test and drew a line between that and the PhD holders who, in his view, "tend to be a bit dull."
Cantina's product portfolio (the ginz platform with about 600K recent interactions across 171 bots, and the Pandie character ecosystem handling about 342K interactions across 93 bots) demands engineers who understand how AI characters behave in real conversations. The platform's documented interaction counts (from the top bot at over 232K recent interactions down to individual characters in the hundreds of thousands) signal a live testing ground, and hiring for ML and infrastructure roles suggests the company wants builders who have shipped something visible. The PropTech Founder said the "main complaint" from candidates interviewing at AI companies is that "there's a lot of talented ML and AI engineers PhDs etc. But they lack the practical software engineering skills and also the soft skills." Cantina's screen appears designed to catch that gap.
The preparation path the PropTech Founder outlined (open-source intelligence on the company, the job description, the interviewer, and running mock interviews through large language models) suggests Cantina's process includes technical assessments that reward candidates who have done their homework. "Dump all this data into your favorite large language model along with your resume and say I'm interviewing with this company," the PropTech Founder prescribed, and the advice to use AI for ruthless mock feedback suggests the interview itself probes depth of understanding rather than memorized answers. The PropTech Founder said the AI-generated questions "will actually pop up in your interview," positioning the screen as one that tests applied knowledge, not rote recall.
Cultural alignment, in this framework, is concrete. The PropTech Founder put it bluntly: "people want to work with other humans that they vibe with," and advised candidates to research the interviewer's personal interests (perhaps they play tennis) and find a natural opening. He also emphasized enthusiasm as a learnable skill: "the bar is very low to excite people," meaning a candidate who shows genuine engagement can clear a hurdle that technical scores alone might not. The PropTech Founder said the industry wants people "who are like quote unquote cracked" — "by being different from the stream, you kind of show those qualities of someone who's cracked."
The screening criteria described here come from a third-party interview-prep source, not from Cantina's own published hiring guidelines. Cantina has not publicly detailed its interview stages, so the characterization draws on the PropTech Founder's reported experience and the observable product demands of a company whose documented interaction counts across individual bots signal a platform that rewards engineers who can make AI characters feel alive in conversation. Candidates who bring shipped project portfolios and a personality that sparks engagement, rather than a pedigree alone, are the profile Cantina's process appears to select for.
What Applicants Say — and What They Don't
Glassdoor reviews, Reddit threads, and Blind posts naming Cantina's screen directly did not surface in the research. The closest available signal comes from the broader patterns job-seekers report across AI startups and from the limited candidate-facing data on the Zero G Talent board.
The board shows one fresh Cantina listing in the past week, and Cantina competes in the upper tier of startup compensation, which means the application pool is likely volume-heavy. A high volume of applicants creates a bottleneck, and the board's emphasis on shipped project work as a screening gate tells candidates that a polished portfolio matters more than a pedigree from a well-known institution.
A PropTech Founder interview prep video surfaced career-advice framing that job-seekers commonly cite when navigating opaque screening processes. One commenter said, "I suggest addressing them" (a reference, in context, to proactively surfacing project evidence during the application). Another advised, "I would recommend waiting till you have leverage," reflecting a widespread sentiment among candidates who feel that early-stage AI screens can feel like an interrogation without reciprocity. A third suggested, "I would ask the manager," pointing to a preference for direct escalation over passive waiting. And a fourth noted, "you want to make sure that when you're asking you ask in a positive way," underscoring the interpersonal calibration that candidates at companies emphasizing cultural alignment report as decisive. These are not Cantina-specific quotes and should not be read as verified reviews. They are the closest available signal of what job-seekers in the AI space are discussing about how to get past a screen that weighs project work and fit equally.
The gap between what candidates report in aggregate and what is documented for Cantina specifically is real. AI startups generally receive hundreds of applications per technical opening, and candidates on platforms like Blind have noted that the initial screen at such companies often hinges on a take-home project or a live coding session that tests the exact kind of hands-on AI work Cantina says it values. Candidates who have built and shipped a model, even a small one, tend to report higher confidence going into those screens, while those whose experience is mostly theoretical describe the process as opaque and stressful. This aligns with the main thread: Cantina's shift toward prioritizing shipped project work raises the bar, and job-seekers are actively recalibrating how they present themselves in response.
The research leaves open whether Cantina's process feels more rigorous or more opaque than the typical AI startup screen. The PropTech Founder content is a generic discussion, not a Cantina-specific thread. Without a substantive body of candidate reviews from Glassdoor, Reddit, or Blind tied to this employer, the candidate experience section remains qualitative. The takeaway for job-seekers is pragmatic: prepare a portfolio that shows end-to-end AI work, and approach the cultural-alignment portion of the screen as a two-way conversation — a step that the available candidate-facing advice, however general, consistently surfaces.
The AI Talent Market Is Squeezing Everyone
Cantina's current hiring push is not an isolated move. It sits inside a labor market where AI talent scarcity has become the defining constraint for early-stage technology companies. HeroHunt's 2024 recruitment data recorded nearly 331,000 active postings for AI roles and AI skills, a 71% year-over-year increase and the fastest-growing skill category in the market. That same year, 75% of employers worldwide reported difficulty filling roles (a record in the survey's history), and the United States sat at 70%. With the unemployment rate hovering around 2.5%, essentially everyone competent is already employed, so every hire is a poach. For startups competing against well-funded incumbents, the math is punishing.
The premium on AI skills has compounded sharply. PwC's measured AI skills premium went from up to 25% in 2024 to 56% in 2025, per HeroHunt's data. A candidate with genuine AI skills in 2024 could expect a meaningfully better offer somewhere else, which shortened the window between first contact and a lost candidate. As HeroHunt put it, the AI premium compounds this: every hire is a race against a competing offer that arrives faster than the screening process can move.
AI model and application development (20%) and AI literacy (19%) became the hardest skills to find globally in 2026, HeroHunt found — skills that barely registered as a category three years earlier. By December 2025, about 4% of U.S. job postings mentioned AI terms, and roughly 9% of HR job postings mention AI, per High5's tracking. The demand side has shifted dramatically; the supply side has not kept pace.
Startups are responding by tightening their screens, and the tools they use reflect that urgency. The percentage of all firms that incorporate AI into recruitment has nearly doubled in just one year, jumping from 26% in a 2023 study to 53% in 2024, per Eightfold's HR Future of AI and Recruitment Technologies report. Among the largest organizations, 60% now use AI in HR, against roughly a third of small and midsize ones — a gap that matters because startups are the ones feeling the squeeze most acutely. Eightfold's data found that 20% of recruitment technology leaders use AI to a "large extent," while the number of laggards using AI to that extent is just 1%.
Yet the efficiency gains come with a quality problem. Eighty-eight percent of organizations cite time savings and efficiency as the primary reason for using AI in recruiting, High5 reported — nine in ten adopters got time back, but fewer than one in four got better candidates. AI in recruiting was overwhelmingly an efficiency technology rather than a quality one, as HeroHunt's analysis concluded. Recruiters worried that algorithmic screening discards non-standard but capable candidates. Fifty-four percent of employers would rehire only 79% or less of the employees they hired in the last year, Eightfold found, and only 24% would rehire at least 90%. The gap between "fast" and "right" is where Cantina's tightening makes sense.
The candidate side of this dynamic adds pressure. Sixty-six percent of job seekers say they will not apply to a job if AI is used in the hiring decision, and 70% of women expressed the same sentiments, Fitsmallbusiness reported. Only 8% of U.S. job seekers believe AI makes hiring more fair, and trust in hiring has declined over the past year (46% of job seekers say so, with 42% attributing the drop specifically to AI).
The result is a structural shift toward what Cantina is doing: prioritizing that evidence and alignment over traditional credentials. These criteria are harder to fake at scale and harder for an algorithm to misinterpret. For job-seekers tracking this space, the implication is concrete — a portfolio of real AI project work matters more than a résumé that lists where you worked, and cultural fit is no longer a soft preference but a filter that separates candidates who reach the interview from those who do not. Zero G Talent's board data for Cantina reflects this shift: the company's salaried roles carry a competitive band with a median in the upper tier, and the newest listing expects candidates to show hands-on AI project work. That pairing (competitive pay plus a raised bar for demonstrable skill) is the market's fingerprint on Cantina's hiring surge.
New Hires Are Building Airtime
The open roles Cantina is filling are not generic headcount — they map directly to the company's product ambitions. The job listings point to a concentrated build-out around real-time video generation, text-to-speech, and the data infrastructure that makes both possible. A Member of Technical Staff, Real-Time Video Generation role sits alongside a Machine Learning Engineer, TTS position, and a Member of Technical Staff, Data & ML Infrastructure for Video Models role. Together with the Product Director, Video Products opening (California) and the Staff Software Engineer, Backend posting (Los Angeles or San Francisco), the pattern is clear: Cantina is staffing the full stack of a product pipeline that turns generated characters and voices into interactive experiences at scale.
The product Cantina is building around these hires is what the company calls social AI characters (entities that "converse, perform, and interact with anyone," per its own description). The tagline "Create characters. Make videos. Go viral." signals that the output is meant to be consumer-facing and shareable, not a behind-the-scenes enterprise tool. This is a deliberate product posture. The company's career page describes itself as a place "founded to do the impossible, consistently," and its Ashby job page frames the mission as developing "advanced real-time models that push the boundaries of expression, personality, and realism." The new roles are the engineering muscle behind that stated ambition.
The Airtime feature (Cantina's name for its real-time interactive video product) sits at the center of this hiring wave. The real-time video generation role, combined with the TTS engineering position and the backend infrastructure work, maps onto what Airtime demands: low-latency character speech, personality-consistent responses, and video output that feels live rather than pre-rendered. That role is especially telling. Building the infrastructure to support real-time generation at scale is not a side project; it is the backbone of a product that must handle concurrent interactions without degrading the user experience. The Android Engineer postings at senior and staff levels further suggest Cantina is investing in the mobile delivery layer, because Airtime's audience is mobile-first.
The timing of the hires reinforces that this is a push, not a slow drift. The board added a fresh Cantina listing just this past week, and the newest postings sit at the upper end of the board's typical range, with bands reaching $200,000–$265,000/year. That premium is consistent with a company that needs to attract practitioners who already have hands-on AI project work in real-time generation and TTS, not train them from scratch. The Product Director, Video Products role adds a layer of product leadership that suggests Airtime is moving from prototype toward a structured release cadence.
Cantina's internal framing reinforces this read. "Cantina Time," the company's career philosophy, describes the process of taking "a hunch and turn it into a compelling solution." That language implies velocity (short cycles from hypothesis to shipped feature), which is exactly the tempo a real-time social AI product requires. The push spans AI, engineering, and product, meaning the company is not just adding individual contributors but building the organizational structure to sustain iterative development.
The available data does not yet show how the Airtime product itself has changed since the hiring surge began. The roles describe capabilities, not shipped features, so the link between the openings and a specific product milestone remains inferential, not confirmed. The research supports the conclusion that Cantina is building toward a real-time, character-driven video product at scale, but the precise shape of Airtime's next iteration, its launch timeline, or its user metrics are not documented in the sources available.
How Does Cantina Compare?
To understand whether Cantina's screening approach stands out, it helps to compare it against a cluster of AI-native companies operating in adjacent territory. The most direct peer comparison surfaces from a Reddit post in r/AI_Chatbot_Master, where a user described Cantina as feeling "like CAI" (shorthand for Character.AI) and offered to share a deeper comparison after further testing (/r/AI_Chatbot_Master). That informal observation lines up with structural similarities: both companies build AI companion products, both hire for ML and engineering roles, and both compete for the same talent pool of candidates who can demonstrate applied AI work.
Character.AI's hiring process provides the sharpest benchmark. Dataford's interview guide database lists 7 roles at Character.AI with documented screening stages, including DevOps Engineer, Machine Learning Engineer, Product Manager, and Project Manager. By contrast, the same database tracks 32 guides for OpenAI, 30 for ByteDance, and 29 for Uber — more than four times the coverage it gives Cantina, which does not yet appear in the index. That gap itself tells a story: smaller, earlier-stage companies tend to have less formalized, less publicly dissected hiring pipelines.
Character.AI's process carries a difficulty rating of 4.4 out of 10, based on 10 reported interviews, and the company describes its own hiring as selective (mission-driven, remote-first, and built around a smaller team than peer AI labs). TechInterview.org confirms this framing, noting that the process "reflects the company's distinctive culture." Cantina's own approach, which prioritizes that evidence and alignment over formal credentials, operates in a similar spirit: smaller scale, product-focused, and less concerned with pedigree than with what a candidate has actually built.
The compensation data reinforces the comparison. Cantina's board salary band runs competitive with peers, with a median toward the upper end of startup pay. Character.AI's reported median sits at $127k, with senior-level total comp ranging from roughly $143k–$175k on the lower end to $395k–$466k at the top. Both companies are paying in the same competitive band for AI talent, which means they are drawing from the same pool of candidates and competing for the same people.
Hugging Face offers a useful contrast. The organization's interview guides cover 5 roles, and TechInterview.org describes its hiring as mission-driven around open-source AI, remote-first, and selective (a profile that overlaps with Character.AI's and, to a degree, with Cantina's). Replika, another AI companion company, has minimal public screening data: 1 interview question and 1 review posted anonymously on Glassdoor. The scarcity of data on Replika's process mirrors Cantina's lack of Dataford coverage, suggesting that smaller AI-native companies in this niche tend to keep their screening details private.
What emerges from the comparison is that Cantina's approach is not unusual in substance (multiple AI startups in the companion and open-source space prioritize mission alignment and hands-on project work) but it is distinctive in its timing and visibility. Cantina is scaling its team deliberately, with a fresh posting at the top of the board's typical band, signaling that growth is intentional rather than reckless. For candidates navigating this environment, the implication is straightforward: that portfolio matters more than a familiar company name on a résumé, and cultural fit is a hard gate, not a soft preference.
What This Story Does Not Cover
The numbers in this piece (18 roles, $225,000 median, 331,000 AI postings, 71% year-over-year growth) describe what is visible: the postings, the boards, the survey data. What stays hidden is the other side of the screen. Cantina's process rewards personality and shipped project work, but it also filters out candidates whose strengths don't map to a single interviewer's definition of "vibe." The same tightening that elevates demonstrable work can close a door on someone who has the skills but not the performative instinct the screen rewards. That tension — between merit and chemistry, between what a candidate built and how they made someone feel — is the hidden architecture behind every "cultural fit" gate, and it is the part of this story no board, no survey, and no interview guide can fully capture.
Working in frontier tech? Zero G Talent tracks the openings: see every open Cantina role, browse frontier tech jobs, the companies hiring, and the people building the field.