Careers · Join us
Join us as a Product Engineer.
Build real products. Not just code.
At GetPost Labs, we build complex business workflow products where humans and AI agents work together. We don’t just write code — we solve real, messy problems.
We are looking for engineers who want to become proper Product Engineers — people who can understand actual problems, make smart trade-offs, design systems, and take full ownership of what they build.
Product Engineering Mentorship Programme
See the full Product Engineering programme.
The index of everything the programme covers — what product engineering is, the RegIntel reference project, and the four real-world project teams, each with its explainer.
What is a product engineer?
A product engineer doesn’t just write code — they own the problem. They understand the business, design the system, make the trade-offs, and ship software that works.
From first principles to production, and everything in between. This is the kind of engineer we want you to become.
How we work
Humans and AI agents, building together.
We build real business applications using a human + AI engineering model. AI agents help us move faster — planning, reading code, generating first drafts, writing tests and exploring options. But humans still own the judgement.
You’ll learn to use AI as a serious engineering tool, not as a shortcut. That means understanding the problem, planning before coding, reviewing AI output carefully, testing what you build, and explaining your decisions clearly.
Why this matters
The goal is not just to write code faster. The goal is to build better product outcomes with stronger thinking.
We use tools like Claude Code and similar agentic development tools — but we don’t blindly trust AI output. We plan first, give the AI proper project context, review what it produces, test the result, and make sure the final work is reliable, secure and understandable.
Choose your track
Pick one area to go deep.
You don’t need to master everything at once. Choose one area where you want to go deep, while still building a broad product engineering foundation.
Before you pick a track: the mindset comes first. Understand the problem before you solve it — read Don’t Be a Problem Solver — Not at First and Bring Back Mathematics.
Full Stack Engineering
Build user-facing product features end-to-end — screens, APIs, data model, validation, testing and deployment.
Platform Engineering
Work on the cloud, identity, infrastructure, pipelines, environments, secrets, networking and reliability every product depends on.
Data Science
Turn messy, real-world data into evidence, analysis, dashboards and decision support business users can trust.
AI / LLM Engineering
Build with LLMs the way real products do — agents, retrieval, prompts, guardrails, tool use, evaluation and human review.
Current Openings
Three ways in.
Three ways into product engineering at GetPost Labs — two entry points to learn the craft, and an experienced role for engineers who already ship. Where you start depends on where you are today.
Product Engineer Intern
Entry level · structured first stepYour first serious step into product engineering. Pick one of the four areas, learn its fundamentals deeply, and contribute to real product slices under guidance. This is not a toy internship — you’ll read real code, understand real product problems, and learn how professional engineering work actually happens.
- Pick one core area to focus on
- Mentor-led work and regular check-ins
- Build small but real product slices
- Learn the agentic way — plan, prompt, review, test, ship
Product Engineer Trainee — Lex-AML
Hands-on · live product workA hands-on traineeship on Lex-AML, our live AML/CTF compliance product. You’ll work on user-facing features, data flows, APIs, bug fixes and product improvements under experienced guidance — learning how a real compliance product works and how to own a small slice end-to-end.
- Ship real code through the team’s CI/CD pipeline
- Read production code deeply; fix bugs at the root
- Work with AI agents and human review together
- Own quality, performance and reliability for your slice
Product Engineer — Lex-AML
Experienced · senior product engineerFor engineers who already ship end-to-end. You code well across Spring Boot / Java, Angular + TypeScript, MongoDB and AWS — but care more about whether a feature solves the user’s problem than about the framework. The cycle from idea to production is short: you’ll see your work in front of users and help define a new category of enterprise compliance software.
- Ship user-facing features end-to-end — frontend, backend, data, infra
- Read AML/CTF regulation deeply enough to push back on requirements
- Drive architectural decisions across backend, frontend, data and cloud
- Mentor mid-level engineers and raise the engineering bar
All roles are remote-first — with real check-ins, real code reviews, and real ownership from day one.
What you’ll do
Real product work, under guidance.
You’ll contribute to real product work under experienced guidance. Depending on your track, you may:
- Build user-facing features across frontend, backend, APIs and data models
- Read production code and understand how existing systems work
- Fix bugs by finding root causes, not just patching symptoms
- Write tests and check your own work before asking for review
- Review pull requests and learn how professional code review works
- Ship code through the team’s CI/CD pipeline
- Use AI agents to plan, explore, implement, test and document work
- Learn the domain behind the product, not just the technology
- Communicate clearly with technical and non-technical people
What we offer
What you get in return.
- Real product-building experience
- Direct exposure to regulatory and enterprise systems
- A clear learning path from intern to Product Engineer
- Work with the latest tools and technologies
- Brisbane-based, with a remote-friendly culture
Before you apply
What you need before applying.
You don’t need to know everything. But you need real fundamentals — and the drive to go deep.
You should be able to:
- Explain one project you’ve built — what problem it solved and what you learned
- Write and debug basic code without guessing blindly
- Understand the basics — data structures, algorithms, OOP, Git, HTTP and databases
- Read unfamiliar code and slowly work out what’s happening
- Ask clear questions when you’re stuck
- Use AI tools thoughtfully, while checking the output yourself
- Communicate what you tried, what failed, what you learned, and what you’d do next
If you’ve worked with Java, Spring Boot, MongoDB, React, Angular, AWS, Claude Code or similar tools, that’s a strong plus. If not, we expect you to show that you can learn them seriously.
The bar you’ll grow toward
Grow into a product engineer who understands what’s underneath.
We’re not looking for surface-level tool usage. Over time, you’ll be expected to build strength across these areas — they’re a growth target, not a day-one checklist.
Fundamentals are non-negotiable. We care less about how many tools you’ve touched and more about whether you understand what’s actually happening underneath.
Engineering fundamentals
- Data structures, algorithms, time and space complexity
- Object-oriented programming and clean code
- Java and Spring Boot services
- HTTP methods, status codes, headers, and request/response structure
- Debugging through stack traces, logs, and root-cause analysis
- Git branches, pull requests, reviews, and team workflow
Database & data layer
- SQL fundamentals — joins, aggregations, indexes, and query reasoning
- MongoDB document modelling and queries
- Collections vs tables, documents vs rows
- Embedding vs referencing trade-offs
- When a document store fits, and when a relational database is better
- Query-performance curiosity — not waiting for someone else to fix slow data access
Cloud & platform thinking
- What an instance, a network, a managed service and a VPC actually are
- Core AWS services — Cognito, EC2, ECS, VPC, Secrets Manager, load balancing
- Why “it runs on my laptop” differs from “it runs securely in the cloud”
- Environments, secrets, configuration, deployment and basic reliability thinking
Product engineering judgement
- Reading code with intent
- Understanding why a decision was made, not just what the code does
- Treating every project as a chance to learn the business domain
- Throwing out work that isn’t landing — including your own
- Asking sharp questions early; explaining technical work to non-technical people
AI-native engineering
- Using Claude, Claude Code, Cursor, Copilot or similar tools as engineering assistants
- The difference between AI as a chatbot and AI as a dev tool with project context
- Planning before coding; using AI to challenge assumptions, not blindly generate code
- Reviewing AI output with scepticism
- Tool use, agents, MCP, subagents, Skills, and instruction files like CLAUDE.md
- Using AI to implement, test, document and publish work — not just generate snippets
Strong pluses
Not required before applying — but they’ll help you stand out.
- A shipped project real users have used, even a handful
- Exposure to Angular, React, Java, Spring Boot, Node.js or Python
- Awareness of REST API design — resources, verbs, status codes, idempotency
- Written automated tests — unit, integration or end-to-end
- Hands-on experience with MongoDB, SQL or another database
- Basic AWS or cloud-deployment experience
- Familiarity with AI development workflows — Claude Code, Cursor, Copilot, MCP or instruction files like CLAUDE.md
- Exposure to queues, pub/sub, messaging, events or idempotent processing
- Basic understanding of environments, configuration and secrets
Personal attributes we value
- Strong willingness to learn — treats feedback as input, not insult
- Asks good questions, and asks them early
- Curious about the domain, not just the code
- Comfortable with ambiguity
- Keeps going when something is hard — and asks for help before staying stuck
- Brings ideas and pushes back constructively
- Communicates honestly and clearly
- Cares about quality, even when the task is small
Your first 90 days
What the journey may look like.
Every person and track is different — but this is the shape of how you’ll grow.
Understand the system
Get to know the live, running product — work to understand its core workflow, the repository structure, Git workflow, dev environment, coding standards and how the team uses AI tools. You may start by reading code, identifying issues and fixing them from the basics up, improving docs, writing small tests — and explaining what you learned.
Own a small slice
Pick a set of features and own them — existing work or something new. Work through it carefully: understand the requirement, ask questions, plan the implementation, use AI tools responsibly, write or modify code, test it, and get it reviewed. Improve it through review, feedback, testing and iteration.
Connect product, code & outcome
Start connecting product thinking with engineering execution — how a business problem becomes a workflow, how a workflow becomes screens and APIs, and how a feature gets tested, reviewed, deployed and improved.
What we don’t expect yet
We don’t expect you to know everything before joining:
But we do expect honesty, effort, fundamentals, curiosity, and the ability to keep learning. If you don’t know something, say so — then show us how you’d find out.
How to apply
Tell us the area you want — and how you think.
Email us at australia@getpostlabs.io with a short introduction. We’re not looking for perfect resumes — we’re looking for people who can think clearly, learn deeply, communicate honestly, and keep improving.
Please include
- 1.Which area you’re most interested in — Full Stack, Platform, Data Science or AI / LLM Engineering
- 2.One project you’ve built or contributed to
- 3.A few lines on what it does, why you built it, and what you learned
- 4.Your GitHub, portfolio, LinkedIn — any link that helps us understand your work
- 5.One technical problem you found difficult, and how you tried to solve it
- 6.Any experience using AI tools for software development
Optional — show us how you think
Want to stand out? Send one short example. We care less about polish and more about clarity of thinking.
- A short video walkthrough of a project you built
- A GitHub repository with a short explanation
- A one-page write-up of a product problem and how you’d solve it
- A bug you fixed and how you debugged it
- A feature idea for Lex-AML or another business application
- An example of how you used AI to plan, build, test or improve something
Resources to help you
Before you reach out, go through these — they’ll help you understand what each role is about and answer many of your questions.
All articles and insights, for your research.
See Data Science, AI/LLM, Platform & Full Stack in the context of a real project.
Videos explaining how things actually work in the industry.
Ready? Send a few lines about you and what you’ve built to
australia@getpostlabs.io