Infinitus Automates 350 Million Healthcare Interactions While Hiring Eight

The Hiring Surge

Infinitus Systems is in the middle of one of its most concentrated hiring pushes since the company was founded in 2019. At least eight roles sit open right now across product, engineering, and customer-facing teams. It is a direct bet that the bottleneck in American healthcare is administrative, not clinical, and that Infinitus can keep eating into it with voice agents handling benefit verifications, prior authorization follow-ups, and prescription-status calls on behalf of providers.

The growth lands at a company that already runs a substantial operation: 219 employees on LinkedIn out of 340 Brannan Street in San Francisco, and a platform that has, according to Infinitus, automated more than 350 million healthcare interactions. Zero G Talent's job board shows the most recent wave of listings running through the past week alone. The freshest postings include a lead product manager for the AI Hub in San Francisco, a remote Vendor & Resource Manager, a California-based Forward Deployed Engineer, a remote Implementation Manager, a San Francisco AI Full Stack Engineer, and a remote Customer Success Manager. The geographic split is deliberate: the AI-heavy work stays in San Francisco, where the clinical and engineering teams sit next to the people designing the agent logic, while customer success, implementation, and vendor management open remotely to pull in operators who already know the payor and provider landscape.

The timing lines up with what the company has been signaling publicly for months. The company's LinkedIn account has welcomed "the newest Infinauts to our team" and ended at least one post with a pointed nudge: "It could be you in our next post. We're hiring!" The company has also published a recap of "Infinicamp 2026," an offsite held in Santa Rosa framed as preparation for "the year ahead," a signal that leadership is treating 2026-2027 as a scaling year, not a steady-state one. The product side is also shipping at a pace that requires more hands: LinkedIn updates have outlined platform changes in a single month, including new Optum Rx and CVS API integrations and faster Medicare Advantage C-SNP provider verifications.

The size of the company matters here. Infinitus still sits in the 51-to-200-employee band on LinkedIn, yet its tools already run at half of the Fortune 50 healthcare companies and 8 of the top 10 pharma manufacturers, per the company's own marketing. That mismatch (a relatively small team running software that touches Amgen, UnitedHealthcare, Aetna, Novartis, Pfizer, GSK, Johnson & Johnson, and Walmart) explains why every hire lands with more weight than it would at a typical Series-B SaaS shop. Each new engineer or product manager joins a roster whose downtime directly translates into hold time on a benefit-verification call somewhere in the country.

What changes for applicants is the bar. With eight roles open at once and a 219-person team to defend against bloat, Infinitus is not hiring to fill seats. It is hiring to compound capacity on a platform whose customers include some of the most regulated buyers in the U.S. economy. The screening that follows this surge is built for people who can already read a clinical workflow and write production code in the same week, a domain-fluency filter sharpened by a workforce problem the founders have described bluntly: an aging population growing while the healthcare workforce shrinks and burns out, with 69% of healthcare workers in a company survey saying administrative tasks get in the way of proper patient care.

Teams and Functions Being Expanded

Infinitus' current hiring slate, while only eight named postings on the surface, maps directly onto four functions the company has been publicly building toward: AI and machine learning engineering, full-stack software engineering, product management, and forward-deployed customer implementation. The shape of the open roles reveals which muscles Infinitus is trying to grow.

The AI engineering bench is the most visible priority. The AI Full Stack Engineer role, based in San Francisco, signals that the company is still building core model and agent infrastructure in-house rather than treating AI as a thin layer on top of third-party APIs. That hire sits inside a product surface that now includes Infinitus Studio, the no-code agent builder launched in April 2026, the next-generation clinical voice agents released at the end of 2025, and the broader patient-facing conversational stack the firm runs today. With the platform reporting that same interaction volume powered as of August 2026, the engineering effort behind maintaining and extending those agents is not theoretical.

Software engineering as a discipline shows up alongside the AI work. The Forward Deployed Engineer role, listed for California, blurs the line between classic services engineering and field deployment: these engineers ship production code inside customer environments, which means core engineering fundamentals are non-negotiable even for client-facing hires. It is a strong indicator that Infinitus still treats every customer deployment as a software problem, not a configuration exercise. Studio's launch material itself called out that "what works in a demo can fail in real-world deployment," and the Forward Deployed Engineer seat is the human answer to that risk.

Product management gets a dedicated, senior-level investment. The Lead Product Manager for the AI Hub position, also based in San Francisco, suggests Infinitus is formalizing a product owner for the Studio and agent-platform layer rather than letting engineering steer the roadmap by default. That is a meaningful organizational shift for a company that, as recently as its 2021 Series B, was still mostly framing itself as a VoiceRPA vendor; owning an "AI Hub" PM seat implies a multi-product surface that needs its own strategy, pricing, and customer narrative.

Customer-facing operations round out the slate and connect the engineering hires to actual revenue. A Customer Success Manager and an Implementation Manager, both remote, sit alongside the forward-deployed engineering role to handle the lifecycle once a health plan or pharma manufacturer signs on. The August 2026 launch of the "AI-first, human-backed hub solution to replace specialty pharma's legacy call-center infrastructure" makes the operational stakes obvious: implementations now span entire call-center workflows, not single use cases.

The one functional gap in the current board data is clinical operations itself. Infinitus' marketing continues to emphasize clinical awareness, HIPAA-compliant evaluations, and a proprietary five-pillar clinical quality framework, but no dedicated clinical-operations or clinical-informatics role appears on the active list. That may reflect where hiring sits today rather than the full picture; the company's safety and trust claims still require clinicians, informaticists, and domain experts somewhere in the org, and the screening bar described in the rest of this article is unlikely to be sustainable without them.

Inside the Screen

Infinitus isn't hiring generalists and hoping they learn healthcare on the job. The company's pitch to candidates, and the language running through its engineering blog and LinkedIn posts, draws a hard line around one principle: voice agents that handle benefit verifications and prescription refills can't afford to guess. A reposting from the company's LinkedIn account makes the stakes literal — "'15' versus '50' is a one-character difference that can change a medication dose" — which sets the tone for what recruiters screen against before a resume reaches a hiring manager.

That posture shapes the funnel. The first filter is domain fluency. Eight of the top 10 pharma manufacturers and more than half of the Fortune 50 healthcare companies run on Infinitus today, and the platform has automated over 200 million minutes of clinical and administrative conversation. Anyone joining an engineering or product pod needs to arrive with enough healthcare context to defend design decisions on day one, not absorb them over six months. A senior director quoted on Infinitus's site put the standard plainly: "You know case management really well, and that's what we want. I don't want to have to teach somebody in a company how to do this." That sentiment is the filter.

The second filter is engineering rigor under clinical constraints. Infinitus's marketing leans on a contrast it draws with competitors: "fully scripted, which means safe but rigid; or fully generative, which means flexible but unpredictable. Neither answer is good enough for healthcare." Building in that middle ground, what the company calls its patent-pending Agentic Response Control, requires engineers who can ship production voice systems, not just prototypes. A hiring panel wants candidates who can talk fluently about latency budgets, ASR error modes, and the tradeoffs between latency and verbatim-script fidelity. A recent engineering post on voice selection for healthcare AI agents signals that voice quality is treated with the same rigor as any other production system. Candidates who frame voice as a UX afterthought tend not to clear it.

Assessment structure follows the risk profile. Live coding and system design evaluate software fundamentals; a separate clinical-reasoning round, often run with product or operations staff, checks whether candidates can reason about prior-authorization workflows, MBI lookups, or Medicare Advantage enrollment without hand-holding. Behavioral interviews probe the harder questions: can you hold the line on a safety guardrail when a customer asks you to relax it? The company has built continuous clinical evaluation into its product: "every agent is continuously evaluated by a clinical team of trained doctors and nurses, across professionalism, empathy, information accuracy, privacy, and clinical safety. Before, during, and after every call." So interviewers want evidence that candidates will treat those checks as load-bearing, not paperwork.

Product and customer-facing roles screen for a different blend. The Lead Product Manager seat on the AI Hub sits at the intersection of agent design and clinical workflow, so candidates are evaluated on their ability to translate a fragmented payer call into a clean product spec. Forward-deployed and implementation roles, several of which are open remotely, are screened on operational pragmatism: can this person take a hospital system's tangled IVR tree and get it dialed through on day one?

One tension is worth flagging. Infinitus publicly insists that "AI won't replace people. It will help healthcare regain its humanity," yet the same engineering posts celebrate agents that "place more calls in a day than a human." Candidates should expect interviewers to test whether they can hold both ideas at once, and pick the human-first framing when the math pushes the other way.

How Candidates Read the Hiring Standard

Infinitus doesn't publish interviewer rosters or a formal hiring philosophy deck, so the clearest window into what its hiring managers actually weigh comes from how the company talks about its work publicly — to customers, to clinicians, and in a 2021 Forbes profile of its early team. The pattern is consistent: domain fluency first, engineering rigor second, and a long-horizon mindset to anchor both.

The technical bar is framed just as directly as the domain one. Forbes reported that Infinitus' voice agent "Eva" had handled around 250,000 phone calls to major insurers in its first 18 months and had helped 35,000 healthcare providers, work that requires production-grade reliability, not prototype demos. LinkedIn posts from the company in late 2025 and 2026 lean hard into operational metrics: 6M+ automated calls to payors, patients, and providers, more than 200M minutes of conversation automated, and a self-reported 0% under-triage rate. Candidates who walk in pointing at research papers without having shipped something that survives a hold-time queue or an IVR tree are unlikely to advance.

A second, less obvious filter shows up in the company's repeated language about human-AI collaboration. The same "AI won't replace people" line that frames the engineering posts shows up in a Newsweek feature casting them as "hospitals' newest 'employees'." For a hiring manager, that translates into a soft filter against candidates who frame automation as a headcount story rather than a coordination one. The customer language reinforces it: teams say they can "service our patients faster" and "focus on members with active and immediate needs" because Infinitus took the phone queue off their plate.

Finally, the horizon. CEO Ankit Jain's recent book promotion frames Infinitus as a "30-year horizon" company, with messaging that distinguishes the work "ahead of us" from current AI hype. That tone seeps into the interview process. A LinkedIn post quoting Laura Chavaree on adverse-event documentation — "It's a huge a-ha and a very exciting opportunity" — is the kind of internal voice that signals what kind of problem-framing the company rewards. Candidates who can speak fluently about benefit verification, MBI lookups, specialty pharmacy triage, and the difference between a refill reminder and a drug-interaction question will read as obvious fits. Candidates who can't will find the screen closing early.

How This Compares to the Broader Healthcare-AI Market

Infinitus is not hiring into a vacuum. Across the healthcare-AI sector, capital, policy, and labor demand are all tilting toward the same bet: automation of back-office and patient-service workflows is the next major efficiency frontier. Deloitte's 2026 Global Health Care Outlook, drawing on a survey of 180 C-suite executives from large health systems across six countries, sketches where the money is going.

Infinitus' eight-role expansion, executed from a San Francisco base with a remote-friendly posture, sits inside that North American concentration rather than at its edge. The gap between "select areas" and enterprise rollout is itself a hiring signal: every system trying to climb from one to the other needs product managers who understand clinical workflows, forward-deployed engineers who can sit inside a hospital's stack, and AI full-stack engineers who can ship against regulated environments. Infinitus' current roster maps onto exactly that profile.

Regulatory pressure is doing the same work as capital. The EU AI Act, in force since August 2024, requires nearly all AI-enabled medical devices, diagnostic algorithms, and decision-support tools to undergo mandatory risk-management review. US health-system leaders are more cautious than their global peers — Deloitte cites uncertainty around tariffs, drug pricing, and regulatory changes — but the direction of travel is the same: more scrutiny, more documentation, more demand for engineers and product people who can build defensibly. Infinitus' tightening of its screening to favor healthcare-domain experience reads as a direct response to that compliance load.

The labor backdrop makes the urgency sharper. The World Health Organization projects a shortage of 4.5 million nurses by 2030, and 40% of UK general practitioners expect to leave the profession within five years, per the same Deloitte outlook. Healthcare-AI vendors are selling into a buyer base that is losing clinical capacity every quarter, which compresses sales cycles for tools that demonstrably free up human time. That pressure shows up in the kind of roles Infinitus is prioritizing: customer success, implementation, and forward deployment, functions that convert a sale into measured time saved on the other end.

The macro hiring market is weak in ways that help Infinitus. Per the New York City Comptroller's February 2026 jobs report, US private-sector employment grew only 0.3% across 2025, averaging 31,000 jobs per month. Healthcare and social assistance was the only sector with meaningful gains, locally and nationally, and AI usage is concentrated in higher-paying white-collar occupations, while healthcare and retail have seen little automation so far. That asymmetry is what Infinitus is funding: it is building the automation layer that healthcare has not yet absorbed, and it is hiring the engineers to do it while the broader tech labor market loosens.

Tactics to Pass the Screen

The screen at Infinitus rewards specificity. Generic ML resumes, the kind that list "PyTorch, TensorFlow, NLP, LLMs" without a healthcare story attached, get filtered out fast. The company's AI agent operates in a tightly defined domain: automated phone calls to more than 200 payors and pharmacy benefit managers, navigating interactive voice response trees, extracting up to 150 data points per call, and pushing back when a representative gives wrong or incomplete information. Hiring managers want candidates who can speak to that world in concrete terms, not in abstractions.

The first move is to read the role back to the company. That same San Francisco PM seat is a different pitch than a Forward Deployed Engineer covering California implementations or an Implementation Manager working remote. Each of the active Infinitus listings on this job board names a function. Tailor the application to that function's actual problems.

Then show the healthcare domain work. Infinitus' models are trained on millions of past calls and grounded in a knowledge graph covering coverage specifics for over 1,000 therapies, procedures, and medications. Candidates who have touched claims data, pharmacy refill patterns, prior-authorization workflows, or benefit-verification queues will read as immediately credible. The Cencora case study quantifies the stakes: 470,000 benefit verifications shifted to automated processes in 2023, supporting 600,000 patients, with the agent running 4x faster than the manual baseline. Anyone who can talk about how those numbers get produced (the multimodal dialogue breakdown, the NLP stack handling multi-turn conversations, the structured outputs that feed downstream systems) signals they understand the product, not just the marketing.

Pair that with engineering fundamentals. The screen tests for both. The candidate who can describe how a language model handles a 30-minute call without losing context, how a knowledge graph gets updated when a payor changes a coverage rule, or how an evaluation set is built from real call recordings has a clearer path than the candidate who lists frameworks but cannot explain the system. The technical bar is the bar of someone who could be put on call to debug an agent's failure mode during reverification season, when volume spikes to ten times the baseline.

Quantify impact wherever possible. The Cencora engagement produced a 24% increase in dormant patients starting treatment, an 8% increase in re-initiation of therapy among high-non-adherence patients, and a 25% reduction in nurse call interventions. Mirror that vocabulary. If you shipped a model, say what it moved — calls per hour, denials avoided, hours of manual work removed — not that you "improved efficiency."

Finally, prepare to discuss the human handoff. Infinitus' pitch is not that the AI replaces staff; it is that human representatives take the complex cases the agent routes to them. Candidates who frame their experience around that split (automation handling volume, humans handling nuance) will sound like insiders. Candidates who frame AI as a wholesale labor replacement will sound like they have not read the room.

What the Expansion Says About the Roadmap

The eight roles Infinitus is filling are not back-office overhead. They are the staffing signature of a platform pivoting deeper into the middle of the patient-services workflow, and the job titles themselves sketch the roadmap. A lead product manager for an "AI Hub" anchors the bet that the company's automation engine will be sold, or white-labeled, as a shared capability rather than a single vertical product. The Forward Deployed Engineer and Implementation Manager roles point in the same direction: the company is preparing to embed staff inside customer environments to translate messy payer phone trees and EHR exports into something the system can consume. The Vendor & Resource Manager hints at the operational layer underneath: subcontractors, BPO partners, and the human-in-the-loop fallbacks that still prop up every "autonomous" call when a payer IVR misbehaves.

What is missing from the list is at least as informative. There is no Head of Sales, no marketing lead, no generalist business-development hire. Every posted role touches either the AI platform, the implementation of that platform at a customer, or the clinical and operational glue that keeps calls completing. Read against Infinitus's own positioning — "healthcare AI made by healthcare experts," with the platform handling benefit verification, prior-authorization follow-up, and prescription follow-up calls — the hiring pattern reads as a deliberate tightening of the product surface rather than a sales expansion.

That tightening maps to a recognizable arc in healthcare-AI procurement. Early deployments of voice agents in revenue-cycle operations usually start narrow: one call type, one payer mix, one EHR. The second wave, and the one Infinitus appears to be entering, is about making the system survive contact with the long tail of payer behavior. Every pharmacy benefit manager has a different prior-auth fax-back number; every commercial plan has a different status-line IVR tree. Expanding the Forward Deployed Engineer bench and the Customer Success Manager bench is the labor required to keep that long tail from becoming a support nightmare, while the AI Full Stack Engineer and Lead Product Manager roles push the platform toward handling more of that variability in software rather than in services.

The competitive read is that Infinitus is moving up the stack at the same time it widens the customer base. A platform that can only do benefit verification has to win each pharmacy benefit manager one by one. A platform packaged as an "AI Hub," with reusable components for prior auth and prescription follow-up, can be sold across pharma manufacturers, health plans, and large provider networks (the segments Infinitus already names on its LinkedIn positioning) without re-engineering the core. The Lead Product Manager hire in particular suggests the company is starting to think about how those components are priced, scoped, and combined, which is the work that precedes a real platform motion.

The market implication is that the next year of Infinitus's roadmap will look less like a series of one-off automation wins and more like the assembly of a reusable toolkit. Hiring data has limits as a forecast, and the company has not published a public roadmap, but the role mix — hub PM, forward-deployed engineering, implementation, full-stack AI, customer success, vendor management — is the staffing shape of a company transitioning from professional services plus model to product plus services. Watch the next batch of job postings: if titles shift toward platform engineering, MLOps, and integrations rather than customer-facing roles, the roadmap has hardened.

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