Quality and Automation Engineer

Sydney, hybrid, 2 days in office

We are partnering with a fast-growing, AI-native software company that is preparing to scale its platform as it begins working with paying customers.

They are seeking a Quality and Automation Engineer to join their software engineering team and build their automated quality capability from the ground up.

This is not a traditional QA role where you simply execute test cases and raise defects. You will design the testing infrastructure, automate UAT and release testing, diagnose defects and use agentic coding tools to implement and validate fixes.

The opportunity The company has defined its release and promotion processes, but does not yet have a sophisticated automated test harness.

Your first priority will be to take an existing UAT proof of concept and develop it into a reliable, high-coverage automated testing capability.

You will work with GitHub Actions, TypeScript and AI-driven testing methods to automate critical product journeys, introduce repeatable quality gates and provide trusted evidence that releases are ready to progress through development, UAT and production.

Initially, you will run manual and automated testing in parallel. You will compare the results, identify gaps and strengthen the automation until the business can confidently rely on it for the majority of release testing.

Key responsibilities

• Design and build automated testing infrastructure, scripts and workflows
• Continue developing and complete the existing UAT automation capability
• Build high-coverage automated regression testing across the product
• Use GitHub Actions to orchestrate automated testing pipelines
• Convert manual testing processes into repeatable automated test scripts
• Record test steps, sequences, data, requests, responses and expected outcomes
• Make AI-driven testing more structured and prescriptive
• Run manual and automated testing in parallel during the transition period
• Compare human and automated test results and investigate discrepancies
• Test releases moving from development into UAT and from UAT into production
• Produce clear evidence showing whether quality gates have passed
• Diagnose defects discovered in UAT, production or through customer reports
• Use agentic coding tools to investigate and develop software patches
• Read, assess and validate AI-generated code
• Create pull requests and progress fixes through the GitHub workflow
• Maintain and expand automated testing as the platform evolves
• Support the triage and engineering resolution of customer-reported issues

What you will bring You may come from either of the following backgrounds:

• A Quality Engineer or SDET with strong software engineering capability
• A Software Engineer with strong quality engineering and test automation experience

You will need:

• Direct experience using agentic coding tools to build, modify or debug software
• Experience crafting prompts and technical context to achieve reliable coding outcomes
• An understanding of the risks and common pitfalls of AI-generated code
• Experience building or materially improving automated testing infrastructure
• Experience working within a structured software engineering environment
• Knowledge of quality gates across development, UAT and production
• Experience with CI/CD pipelines and GitHub-based engineering workflows
• The ability to read, understand and assess application code
• Strong judgement when evaluating defects, technical approaches and proposed fixes
• The ability to distinguish between software defects, usability issues and user errors

Technology environment Working knowledge of the following will be valuable:

• TypeScript
• Python
• Prisma
• GitHub
• GitHub Actions
• GitOps
• PostgreSQL

Experience with AWS, Expo, React Native or mobile application testing would be beneficial but is not essential.

Agentic coding experience This role requires more than basic use of AI for code completion.

You should be comfortable using tools such as Claude Code, Cursor or similar agentic coding platforms to:

• Investigate technical problems
• Develop and modify software
• Generate and assess patches
• Review proposed technical approaches
• Identify incorrect assumptions
• Validate that the resulting code is safe and appropriate
• Prevent AI from finding workarounds that hide underlying defects

There will be relatively little fully hand-written coding. Your value will come from understanding software engineering well enough to guide the AI, assess its work and make sound technical decisions.

Why join?

• Build a quality engineering capability from the ground up
• Work within an AI-native engineering environment
• Own testing infrastructure and automation rather than only executing tests
• Work directly within the application codebase to resolve defects
• Help shape how a growing software business releases safely and quickly
• Significant flexibility around seniority
• Initial contract engagement with a genuine intention to convert to permanent employment

This role would suit someone who enjoys working across quality engineering, automation, software development and AI-assisted problem-solving.

We are partnering with a fast-growing, AI-native software company that is preparing to scale its platform as it begins working with paying customers.

They are seeking a Quality and Automation Engineer to join their software engineering team and build their automated quality capability from the ground up.

This is not a traditional QA role where you simply execute test cases and raise defects. You will design the testing infrastructure, automate UAT and release testing, diagnose defects and use agentic coding tools to implement and validate fixes.

The opportunity The company has defined its release and promotion processes, but does not yet have a sophisticated automated test harness.

Your first priority will be to take an existing UAT proof of concept and develop it into a reliable, high-coverage automated testing capability.

You will work with GitHub Actions, TypeScript and AI-driven testing methods to automate critical product journeys, introduce repeatable quality gates and provide trusted evidence that releases are ready to progress through development, UAT and production.

Initially, you will run manual and automated testing in parallel. You will compare the results, identify gaps and strengthen the automation until the business can confidently rely on it for the majority of release testing.

Key responsibilities

• Design and build automated testing infrastructure, scripts and workflows
• Continue developing and complete the existing UAT automation capability
• Build high-coverage automated regression testing across the product
• Use GitHub Actions to orchestrate automated testing pipelines
• Convert manual testing processes into repeatable automated test scripts
• Record test steps, sequences, data, requests, responses and expected outcomes
• Make AI-driven testing more structured and prescriptive
• Run manual and automated testing in parallel during the transition period
• Compare human and automated test results and investigate discrepancies
• Test releases moving from development into UAT and from UAT into production
• Produce clear evidence showing whether quality gates have passed
• Diagnose defects discovered in UAT, production or through customer reports
• Use agentic coding tools to investigate and develop software patches
• Read, assess and validate AI-generated code
• Create pull requests and progress fixes through the GitHub workflow
• Maintain and expand automated testing as the platform evolves
• Support the triage and engineering resolution of customer-reported issues

What you will bring You may come from either of the following backgrounds:

• A Quality Engineer or SDET with strong software engineering capability
• A Software Engineer with strong quality engineering and test automation experience

You will need:

• Direct experience using agentic coding tools to build, modify or debug software
• Experience crafting prompts and technical context to achieve reliable coding outcomes
• An understanding of the risks and common pitfalls of AI-generated code
• Experience building or materially improving automated testing infrastructure
• Experience working within a structured software engineering environment
• Knowledge of quality gates across development, UAT and production
• Experience with CI/CD pipelines and GitHub-based engineering workflows
• The ability to read, understand and assess application code
• Strong judgement when evaluating defects, technical approaches and proposed fixes
• The ability to distinguish between software defects, usability issues and user errors

Technology environment Working knowledge of the following will be valuable:

• TypeScript
• Python
• Prisma
• GitHub
• GitHub Actions
• GitOps
• PostgreSQL

Experience with AWS, Expo, React Native or mobile application testing would be beneficial but is not essential.

Agentic coding experience This role requires more than basic use of AI for code completion.

You should be comfortable using tools such as Claude Code, Cursor or similar agentic coding platforms to:

• Investigate technical problems
• Develop and modify software
• Generate and assess patches
• Review proposed technical approaches
• Identify incorrect assumptions
• Validate that the resulting code is safe and appropriate
• Prevent AI from finding workarounds that hide underlying defects

There will be relatively little fully hand-written coding. Your value will come from understanding software engineering well enough to guide the AI, assess its work and make sound technical decisions.

Why join?

• Build a quality engineering capability from the ground up
• Work within an AI-native engineering environment
• Own testing infrastructure and automation rather than only executing tests
• Work directly within the application codebase to resolve defects
• Help shape how a growing software business releases safely and quickly
• Significant flexibility around seniority
• Initial contract engagement with a genuine intention to convert to permanent employment

This role would suit someone who enjoys working across quality engineering, automation, software development and AI-assisted problem-solving.