Full-time Permanent Mid-level Senior
Posted 24 Sep ****
Description
Position overview
Matrix is building its own product capability in house to extend our reach beyond the classroom.
We are assembling a small team of product builders who ship the software our students, parents and teachers actually use.
This high-priority role is a foundational engineering seat in that recent team as we grow.
You'll report to our Head of Technology and work closely with our Head of Digital Product and the rest of the Product Building team.
You will build and ship Next.js, TypeScript and Node applications with PostgreSQL, integrating Salesforce and Snowflake through a shared business API layer.
Claude Code is the primary development tool and we expect real agentic development fluency on day one.
You will define and operate a modern, AI-first development lifecycle, architecting fully automated CI/CD pipelines, preview environments, automated validation, and safety guardrails that allow autonomous agents to execute end-to-end development safely and rapidly.
The part that makes this role different from a standard engineering seat is the product side.
You will not be handed a finished specification.
You will be in the room when the problem is framed, when the storymap is built, and when we decide what to cut.
We want an engineer who has views on what should be built, argues about scope, and can tell the difference between a hard problem and an expensive one.
You do not need to want to be a product manager.




You do need to be comfortable when the conversation is about students and parents rather than code.
Two honest notes.
The brief will move, because we are working out this way of building as we go.
And prototypes can be rough, but production code is tested and reviewed.
Speed comes from the pipeline, not from skipping the standards.
Key responsibilities
1. Build and ship product in a small team
[Initial priority]
Build and ship Next.js and Node applications, from server components and API routes through to production, using Claude Code and Cursor as your primary tools
Join the storymap and prototype work at the start of a build rather than picking up a finished spec, and push back on scope when the engineering cost does not match the outcome
Integrate with Salesforce, Snowflake and and other Enterprise systems through the shared business API layer so logic is written once and used by every app
Own the review loop on agent output: find the logic errors and security gaps, and decide what to keep, what to rewrite, and what to send back with better context
Ship at least one customer-facing release in your first quarter that real students, parents or teachers use
2.



Define and scale the agentic development lifecycle
[Initial priority]
Design and optimise an end-to-end agentic workflow where requirements translate into passing tests, generated code, preview environments, and production releases at high velocity
Build the automated testing, branch management, and CI/CD automation necessary for AI agents to safely generate PRs and deploy without manual friction, retaining human review at strategic gates
Instrument full observability across agent execution, automated testing, and production deployment to monitor performance, debug failures, and continuously optimize cycle times
Apply the OWASP Top 10 for LLM Applications to any agent-facing surface, and build the guardrails before a student sees it
3. Shape what gets built and prove it worked
Instrument the outcome metrics for what you ship, including first week engagement, activities per week and conversion, and build the dashboard the team reviews
Bring the feasibility view into product decisions early: what is cheap, what is expensive, and what looks small but is a trap
Contribute reusable components and shared patterns so the other builders do not re-solve the same integration twice
What we're looking for
Experience and qualifications
You have shipped Next.js and TypeScript in production, client and server rendered, with real users behind it
5+ years building production software, including at least 12 months where AI agents did meaningful parts of the work etc..
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