About Zania
Zania is building AI agents for enterprise governance, risk, and compliance teams to execute complex workflows with full explainability. Series A, $18M raised from NEA, Anthropic, and Menlo Ventures. Founded by a former founding security engineer at Airbnb and builder of Microsoft Security Copilot. We're not building another dashboard — we're replacing entire workflows with agents that do the work.
Why this role exists
We need an Eng Manager who will own engineering end-to-end: architecture decisions, hiring, shipping cadence, and team culture. This is not a "manage managers" role. This is a hands-on technical leader who scales the team from ~5 to 20+ engineers in the next 12 months while personally ensuring the product is world-class.
What we're looking for — non-negotiables
1. Builder first, manager second. This person was (or could be) the strongest engineer on any team they've been part of. They chose management not because they plateaued technically, but because they were a founder or realized leadership was the highest-leverage way to multiply themselves. They can read a PR, spot an architectural mistake, and rewrite it themselves if needed.
2. Founder DNA. Prior experience as a technical founder or very early engineering leader (employee #1–10) at a startup that shipped real product under real constraints. They know what it means to make decisions with incomplete information, cut scope ruthlessly, and hold a team accountable to weekly ship cycles — not quarterly roadmaps.
3. Proven ability to scale a team from small to 20+. They've hired 10–20 engineers in a compressed timeline without lowering the bar. They have a system for how to source, close, and onboard fast. Ideally they have a personal network of strong engineers in the Bay Area they'd bring with them or recruit from.
4. AI-native mindset. Not "interested in AI" — actively building with LLMs, agents, tool-use, and orchestration frameworks today. They understand prompt engineering, evaluation, context window management, and the difference between demo-ware and production-grade AI systems. Experience with agentic architectures is strongly preferred.
5. Shipping velocity as a core value. They measure engineering health by what's in production, not what's in Jira. They set aggressive timelines and hold the team to them. They know how to unblock engineers, kill unnecessary process, and create a culture where deploying daily is normal.
6. Deep, not wide. We do not want a "mezzanine" leader — someone who operates through layers, delegates everything, and speaks in frameworks. This person goes deep on specific systems, debugs production issues at 2am, and explains architecture tradeoffs to a customer on a sales call.
What you'll do
Own all engineering output: product quality, velocity, reliability, and security.
Build the engineering team from current size to 15–20 in 12 months. Personally involved in every hire.
Set and enforce a shipping culture — weekly demos, continuous deployment, minimal process overhead.
Partner directly with the CEO on product direction, prioritization, and customer-facing technical conversations.
Architect and evolve the platform (LLM orchestration, agentic workflows, compliance automation) for enterprise scale.
Establish lightweight but real engineering practices: CI/CD, observability, on-call, code review standards.
Signals we look for
Founded or co-founded a technical startup (even if it didn't work out).
Scaled an eng team through a Series A/B at a product-led company.
Has shipped AI/ML products to production — not research, not POCs.
Deep in the Bay Area engineering community — knows who to call.
Background in security, infrastructure, or enterprise SaaS is a plus.
Has operated in a domain where correctness matters (fintech, security, health, compliance).
Minimum Qualifications
Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
8+ years of software engineering experience, with at least 3 years in a technical leadership or founding engineering role at a product-led company
Demonstrated experience scaling an engineering team from small to 20+ engineers, with a proven ability to hire, develop, and retain senior technical talent
Deep, hands-on experience shipping AI/ML or agentic systems to production at enterprise scale
Strong written and verbal communication skills — you will partner directly with the CEO and represent engineering in customer-facing technical conversations
Comp
Significant equity + competitive cash — structured to attract a builder, not a caretaker. Reports directly to CEO.
How to apply
Email [email protected] or apply through our careers page.
Skills Required
- Builder-first mentality; able to read, spot architectural mistakes in PRs, and rewrite code when needed
- Prior experience as a technical founder or very early engineering leader (employee #1-10)
- Proven ability to scale an engineering team from small to 20+ hires quickly
- AI-native experience building with LLMs, agents, tool-use, orchestration frameworks, and prompt engineering
- Deep, hands-on experience shipping AI/ML or agentic systems to production at enterprise scale
- 8+ years of software engineering experience
- At least 3 years in a technical leadership or founding engineering role at a product-led company
- Strong written and verbal communication; ability to represent engineering in customer-facing technical conversations
- Experience establishing and operating CI/CD, observability, on-call rotations, and code review standards
- Willingness to be hands-on in production debugging and technical depth (e.g., respond to incidents)
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
- Background in security, infrastructure, or enterprise SaaS
- Deep Bay Area engineering network or ability to recruit locally
- Experience operating in domains where correctness matters (fintech, security, health, compliance)
What We Do
We are transforming Governance, Risk, and Compliance for enterprises and professional service firms using autonomous AI agents. Our solution delivers exceptional accuracy, cuts costs significantly, and shortens a months-long process to just minutes. Achieve compliance more swiftly and with reduced manual effort—enhancing both efficiency and precision.
