Careers at Swif.ai: Teams, Pay and How to Get Hired

Who Gets Hired

Swif.ai builds a unified platform for device management, real-time Shadow IT discovery, and continuous compliance automation. The product supports macOS, Windows, Linux, iOS, and Android through a single console, with zero-touch enrollment via Apple Business Manager, Windows Autopilot, and Android Enterprise. The company serves 100+ customers and targets IT and security leaders evaluating Vanta or Drata integrations who need live device posture.

The team operates across Taiwan, the United States, Canada, Singapore, Vietnam, Indonesia, Japan, Korea, Turkey, and Malaysia. Current open roles on the Zero G Talent board include:

• Backend Developer (Scala/Java): owns the control plane for device enrollment and fleet state at scale
• Backend Engineer (Golang): builds policy enforcement services across macOS, Windows, and Linux
• Frontend Engineer (Vue, Browser Extensions, AI Agent): develops the console and lightweight browser extension that surfaces Shadow SaaS and AI tools across Chrome, Edge, Firefox, and Safari
• DevOps Engineer: runs infrastructure for 24/7 CVE monitoring and compliance evidence pipelines
• Software Testing Engineer (QA): validates silent installer and auto-enrollment paths that bring customers to full device coverage in under 24 hours
• Full Stack Software Engineer Intern [Summer 2026]: for students who have shipped a full-stack feature to production, ideally with device or security context
• Full-Cycle Sales AE: sells into IT and security leaders; MSP motion includes 10% cash-back for partners, 20% for larger MSPs
• Sales Development Representative (SDR): works the same enterprise and MSP pipeline
• Sales Intern [Summer 2026]: supports the sales motion

Founders Angelo (KC) Huang, Angelo Huang, and Xianxian Huang came from operator backgrounds — LeadIQ (scaled to tens of millions in revenue), device security, and Y Combinator founding experience — and screen for people who have felt the pain of manual compliance, Shadow IT blind spots, or broken device enrollment and built something to fix it.

What It Pays

Swif.ai's compensation data comes from nine live postings on that board: seven salaried roles and two internships. The bands are tight for a seed-stage company: $30k–$74k aggregate range, $60k median, as Zero G Talent's board data shows. The spread reflects a globally distributed team where location arbitrage is baked into the offer.

Engineering roles cluster at the top of the band.

All five are remote across the regions listed above.

Sales roles carry wider variance. The Full-Cycle Sales AE lists $30k–$60k (Sunnyvale or remote US); the Sales Development Representative (SDR) runs $40k–$80k across the U.S. and Canada, a higher ceiling that likely reflects variable on-target earnings, according to Zero G Talent's job board. Both roles tie explicitly to the MSP and enterprise motions: 10% cash-back for MSP partners, 20% for larger ones, and a $12/user/month retail price the founder notes is negotiable per deal.

Internships are priced as stipends. The internship pays $500–$2,000/mo (US and Taiwan, remote). The Sales Intern [Summer 2026] lists $300–$1,000, a figure that reads like a typo or token honorarium. Neither posting converts to an annualized band comparable to salaried roles.

Equity and benefits are where the public record goes quiet. The board postings don't publish grant sizes, vesting schedules, or refresh policies. The company's site and the October 2024 demo video mention health coverage tiers by plan — Free: community/email; Growth: next-business-day chat; Enterprise: 24×7 chat/phone plus a dedicated CSM and quarterly security reviews — but those are customer-facing SLAs, not employee benefits. LinkedIn shows a privately held, 51–200-person company headquartered in Sunnyvale with a San Francisco Y Combinator address. Standard for a U.S. entity means 401(k) and health insurance are table stakes, but the research contains no employee-facing confirmation of match percentages, HSA contributions, or equity pool allocation.

The highest bands go to engineers who ship the browser extension that captures Shadow IT, the Golang services that enforce policy across macOS, Windows, and Linux, and the Scala/Java pipelines that ingest device telemetry. Sales roles carrying quota on the MSP and enterprise motion, where the product replaces three to five tools, sit in the same neighborhood. Interns and QA/DevOps roles, while essential, are priced for contribution within a defined scope rather than the end-to-end ownership the founding team screens for.

For a candidate, the takeaway is straightforward: negotiate from the band's midpoint up if you can demonstrate a shipped system that spans device enrollment, policy enforcement, and compliance evidence collection. The board data doesn't show wide published equity upside, but at a YC S20 company with 100+ customers and a product that consolidates MDM, Shadow IT discovery, and audit automation, the equity conversation starts at the offer stage — not in the job description.

How Hiring Works

Swif.ai's public job postings on Y Combinator show a standard interview sequence: application review, screening call, phone or video interviews, in-person or virtual interviews with hiring managers and team members, possible assessments or testing, reference checks, and offer negotiation. The company has not published role-specific loop details, timeline data, or offer rates in the available sources.

Candidates should prepare to discuss concrete project work; the founders' background (LeadIQ, device security, YC) suggests they value demonstrated ownership of production systems over credential collections. The distributed, remote-first team structure means async collaboration and cross-time-zone ownership are daily realities.

Where Work Happens

Swif.ai maintains a single physical office at 880 W Maude Avenue in Sunnyvale, California. Craft.co and Highperformr confirm one office location; ZoomInfo reports 18 total employees; PitchBook and Highperformr report 42–45 total employees as of 2026; LinkedIn shows 51–200. The Sunnyvale office anchors legal, fundraising, enterprise sales, and compliance framing, but most engineering happens in the distributed nodes.

The job board makes the distribution concrete: engineering and sales roles list hiring locations across Taiwan and the other regions previously mentioned. Sales and SDR roles specify Sunnyvale or US/Canada remote. Internships run in US and Taiwan time zones. There is no second office, no lab facility, no integration floor. The "lab" is the endpoint fleet the product manages; the "integration floor" is the CI/CD pipeline that ships the unified agent across Windows, macOS, and Linux.

This structure mirrors the product: a control plane for device management, AI-agent governance, and autonomous remediation designed for remote workforces at scale. The research the team publishes on agent security, covering credential leakage in MCP servers, dead-man's switches in supply-chain malware, and delegation chains between agents, comes from engineers who themselves work across borders, time zones, and trust boundaries.

For a candidate, this means the interview process tests for autonomy and async communication as much as technical depth. The offer letter will not assign you to a desk in Sunnyvale unless you take a go-to-market role. It will assign you to a time-zone band, a GitHub organization, and a set of runtime environments (Windows, macOS, Linux, browser extension sandboxes) where your code ships to production without a staging hallway walk. The physical site matters for offsites, visa anchoring, and the occasional board meeting. The work happens where the agents run.

What It Takes to Thrive

Swif.ai's technical output reads like a threat-modeling journal. Since late 2024 the team has published dozens of LinkedIn posts dissecting real agent compromises: credential theft via instance metadata services, prompt injection that chains into government form submission, MCP server vulnerabilities that exfiltrate PKCE proof keys, and delegation chains where authority drifts toward the union of every credential in the path. The people who write these posts, and the engineers who build the control plane behind them, share a recognizable profile: they treat agent identity as an infrastructure problem, not a prompt-engineering concern.

That framing shapes hiring. The open roles cluster around distributed systems, browser-level instrumentation, and test infrastructure for non-deterministic workloads. But the filter isn't compensation; it's whether a candidate can demonstrate they've owned a system from credential issuance through revocation.

The posts make this explicit. "An agent's execution identity is not a deployment detail. It is the agent's real permission set, whatever its prompt, guardrails, or tool gateway say," the team wrote in an October 2026 analysis of AWS AgentCore. That line captures the mindset: the model's intent is irrelevant; the identity it executes under determines blast radius. Engineers who thrive here have already internalized that distinction. They've scoped credentials to tasks, not environments. They've built allowlists tied to enforceable hooks. They've asked, "If this agent is compromised, can I revoke its identity and find what it wrote into other agents' memory?" and shipped the answer.

The same rigor appears in the front-end and QA roles. The frontend posting calls out browser extensions and AI agents, a combination that only exists when you're instrumenting the agent's execution environment, not just its chat UI. The QA role sits in Taiwan and Vietnam, time-zone aligned with the backend-heavy hiring in those regions. The DevOps role spans Taiwan, Vietnam, Indonesia, Turkey. This is a distributed team by design, not accommodation. The people who last treat async coordination as a systems constraint to engineer around, not a communication tax to complain about.

The founders set the tone. Their public writing doesn't evangelize; it documents failure modes ("Read-only was a sentence in a system prompt. The database never heard it") and the countermeasures that actually held. That culture selects for engineers who prefer a verified commit log over a design doc, who instrument first and announce later, who can explain why revoking everything immediately destroys the endpoint and the evidence on it.

The sales roles carry the same filter. The product is that control plane and AI-agent governance. Selling it requires enough technical fluency to answer a CISO's question about whether every action resolves to an identity the enterprise recognizes, whether permission changes are captured as first-class events with their trigger, whether the trace can be altered after the fact. The reps who ramp fast are the ones who've already read the team's posts and can map the procurement questions to the architecture.

The signal is in the work: 100,000+ lifetime installs of the tensorlake npm package, plus dozens of agent compromises dissected in public posts — 60 local agents discovered on a single Windows scan, six agent–app combinations for hook-based inspection, four for network inspection, nine proxy IPs tracked. The people who thrive look at those numbers and see the next attack surface to harden.

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