Software Engineer

Garnaut Global; City of Melbourne; On-site; AUD 120,000 - 180,000; Full time

Job summary

Garnaut Global in Melbourne seeks a software engineer to design and build the firm’s systems from strategy to code, collaborating with the CTO and a hybrid team of humans and AI agents.

You’ll work across Python, TypeScript, AWS, data platforms, and ML pipelines, owning end-to-end systems. The role rewards versatility, fast learning, and delivering robust, production-ready software.

Qualifications

• Three to five years of professional software engineering experience.

Responsibilities

• Design and build the firm’s systems from strategy to code, working with the CTO and a hybrid team of humans and AI agents.

Skills

Python

TypeScript

Rust

AWS

IaC

DevSecOps

ML pipelines

Data engineering

Database knowledge

CI/CD

Education

Computer Science/Data Science degree

Tools

Aurora/Postgres

Iceberg

Graphs databases

Terraform

Containers

GitHub Enterprise

Job description

We are a global research and advisory firm that focuses on the world’s most consequential geopolitical and strategic matters. Our work informs the decisions of governments, institutions, and enterprises.

We hold ourselves to unusually high standards. Our research is defined by empirical rigour and integrity. Our writing by clarity and authority. Our advisory work by genuine mastery of subject matter.

We are a flat, expert-heavy team. Everyone operates at a high level, takes ownership, and is trusted to exercise judgment.

The Role

You’ll work as part of a team designing and building the firm’s systems — the channels clients use, the research and authoring tools our analysts work in, the data lakehouse and analytics layer underneath, with AI used throughout.

The job runs from strategy to code: you’ll help work out what the firm needs, design it, then build it — working directly with the CTO, the rest of the human tech team, and an ensemble of AI agents.

• Languages: Python and TypeScript (front-end), occasionally Rust.
• Cloud: AWS — many services, IaC and containers throughout.
• Data: Aurora/Postgres, Iceberg, and a range of analytical and graph databases and caches.
• CI/CD: GitHub Enterprise and Actions, with security embedded in the pipeline — reviewed, gated change as the default (DevSecOps).
• AI in the delivery loop, with agents and humans in a hybrid team.

You

We’re looking for someone with three to five years of experience, with strong software engineering fundamentals. You should have broad skills across networks, operating systems, cloud (AWS), databases, and data science.

Nobody’s an expert in everything — we care more about versatility and how fast you pick things up. But we expect that:

• You’ve built something hard. The work you’re proudest of took real engineering — a data platform, a distributed system, an ML pipeline, an inference-backed feature — and you can take us through what made it difficult and how you solved it.
• You move between languages easily. You reach for whichever one fits and don’t get precious about it.
• You understand data, analytics, and ML. You’ve modelled data, built pipelines that held up in production, and worked with ML past the coursework stage. You’ve taken models from a notebook into production yourself.
• You know what’s under the application. E.g. networking, operating systems, cloud primitives, databases. When something breaks a couple of layers down, you can find and fix it.
• You learn fast and get things done — you’d rather ship something and iterate than wait to be handed a spec.
• You use AI well. Claude Code and similar tools are part of how you build, and you can help build the infrastructure AI workloads run on.
• You’re self‑directed and honest with yourself — you find answers, and you know when to go deep and when to ask.
• You take probity and confidentiality seriously.
• Three years’ professional software engineering minimum; the role is pitched around five.
• Production Python and TypeScript — systems you shipped and kept running, not one‑off scripts.
• A system you owned end to end, and can walk through in depth.
• Real data‑engineering experience — pipelines, modelling, serving analytical workloads.
• AWS through IaC — Terraform and containers, with the console as the exception.
• DevSecOps fundamentals — pipeline security, least privilege, secrets hygiene.

Desirable: a degree in Computer Science and/or Data Science.