Stuff is hiring a founding Principal Engineer to build an AI-native news platform from scratch
New Zealand media company Stuff is creating a separate, AI-focused consumer platform and has opened a founding Principal Engineer role to lead the build. This is the first engineering hire and reports directly to the Chief Product Officer, with day-one access to the executive team. The brief: architect it, code it, ship it - then set the standards the future team will follow.
What's being built
Stuff is taking a greenfield approach with no inherited code or legacy systems. The plan is staged: create the technical blueprint, build a testable prototype, then deliver a secure, production-ready V1 that can scale and adapt as the product evolves.
The Principal Engineer will own the end-to-end architecture across services, APIs, data models, security and hosting. They'll make build-versus-buy calls on core components and integrations, and establish conventions that let future teams move quickly without sacrificing safety.
Hands-on from day one
This is not an oversight role. The founding engineer will write the early code, ship features and carry the platform through to production before a wider team is in place. Responsibilities span backend services, frontend experiences, infrastructure, CI/CD and monitoring.
AI-assisted development is expected as a daily practice to accelerate delivery. Data is central: models and pipelines must support analytics, insight and personalisation with privacy, governance and security built in from the start. Reporting is required immediately, with room for future machine learning features.
Executive-level impact
The role sits close to the product and company strategy. The engineer will translate user needs and business goals into concrete technical decisions, communicate progress and trade-offs clearly, and keep execution aligned with the roadmap. They'll also define how engineering works as the team grows, including how AI tools are used and how the group runs experiments and short learning cycles.
Profile they're targeting
- Senior engineers with breadth across backend, frontend, infrastructure and DevOps
- Proven experience architecting cloud-based platforms and shipping greenfield products
- Practical use of AI-assisted dev tools and strong systems thinking
- Comfort operating in fast-moving environments with changing priorities
- Consumer or marketplace experience is a plus
Stuff positions this as work with real reach inside one of New Zealand's most influential media organisations. Expect direct access to decision-makers and meaningful influence over technical choices that affect millions of users.
Why this matters for product leaders
- Separate stack = faster cycles. Breaking from legacy lets product and engineering test, learn and ship without dependencies dragging timelines.
- Stage gates create clarity. Blueprint → Prototype → Production V1 is a clean path for risk reduction and stakeholder alignment.
- Build vs. buy is a product decision. Treat core capabilities as strategic; outsource commodities. Document the reasoning either way.
- Privacy from day zero. Consent flows, data retention, and PII boundaries must be part of the architecture, not a bolt-on. See the NZ Office of the Privacy Commissioner's guidance: privacy.org.nz.
- Make AI a workflow, not a novelty. Define policies for AI-assisted coding, code review and security. For context on developer tooling, see GitHub Copilot docs.
Practical blueprint: what the founding engineer will set up
- Architecture: service boundaries, API standards, data contracts, eventing and identity model
- Quality & reliability: test strategy, CI/CD, SLOs, error budgets and incident response
- Security: authN/authZ, secrets management, threat modelling and secure defaults
- Data platform: analytics-ready schema, consent-aware pipelines, observability of user journeys
- Golden paths: language/runtime choices, templates, scaffolds, linters and docs to keep teams fast and consistent
Questions product teams should answer early
- What is the smallest useful V1 that proves value for readers and the business?
- Which capabilities must be proprietary, and which can we outsource without losing leverage?
- What privacy promises are we making to users, and how do we prove we keep them?
- What are our non-functional baselines (latency, uptime, cost per MAU), and how will we measure them?
- Where will we use AI in the product and in the workflow, and what are the guardrails?
The opportunity
If you're a senior engineer who enjoys building from first principles and partnering closely with product, this role offers rare autonomy and scope. For product leaders, it's a clear example of how to structure a clean, high-velocity build with strong data practices and a tight feedback loop.
Want to level up your team's AI skills while you build? Explore role-based programs here: Complete AI Training - Courses by Job.
Your membership also unlocks:
