ML Engineer

is a pioneer of Australian eCommerce, and the software we build is used by millions of customers every day. You'll join a fast-moving engineering team with real ownership, shipping to production daily and using AI as part of how we work. As a Data Engineer you'll design and run the data and ML pipelines that let teams across Marketing, Purchasing, Logistics and Finance make confident, data-driven decisions. What you'll do:

• Machine Learning Development: Design, build and deploy machine learning models that solve practical business problems, including recommendation systems, demand forecasting, customer segmentation, churn prediction, pricing optimisation and fraud detection.
• Production ML Systems: Build reliable and scalable machine learning services that deliver predictions in both real time and batch environments, ensuring strong performance, reliability and cost efficiency.
• MLOps & Automation: Develop and maintain MLOps pipelines that automate model training, validation, deployment, monitoring and retraining.
• Feature Engineering: Work with large datasets to create robust feature pipelines and reusable datasets that improve model performance and accelerate experimentation.
• LLM & Generative AI Applications: Design, evaluate and deploy AI powered solutions using large language models (LLMs), retrieval systems, agents and emerging AI technologies to enhance customer experiences and internal productivity.
• Model Monitoring & Optimisation: Implement monitoring frameworks to track model performance, drift, accuracy and business impact, continuously improving models in production.
• Cross Functional Collaboration: Partner with Product Managers, Engineers, Analysts and business stakeholders to identify opportunities where machine learning can create measurable value.
• Software Engineering Excellence: Develop solutions in line with software engineering best practices, including Git, CI/CD, trunk based development, testing and observability.
• AI Collaboration: Contribute to experiments with AI and emerging technologies, helping shape how leverages machine learning and automation across the business.

What you'll need:

• Strong Python Skills: Commercial experience building machine learning applications and data pipelines using Python and relevant machine learning libraries.
• Machine Learning Expertise: Hands on experience developing, evaluating and deploying machine learning models in production environments.
• MLOps Experience: Experience building and maintaining model deployment, monitoring and retraining pipelines using modern MLOps practices and tools.
• Data Engineering Foundations: Strong SQL skills and experience working with large datasets, feature engineering workflows and distributed data processing systems.
• Cloud Experience: Hands on experience with cloud platforms, preferably Google Cloud Platform (GCP), including Vertex AI, BigQuery and Cloud Run.
• Software Engineering Best Practices: Strong understanding of Git, CI/CD, testing frameworks, containerisation and production system reliability.
• AI & LLM Experience: Experience working with LLMs, embeddings, vector databases, RAG architectures or AI agents through commercial or personal projects.
• Problem Solving Mindset: A practical engineering approach that balances experimentation and innovation with reliability, scalability and business impact.

Bonus Points

• Recommendation & Forecasting Systems: Experience building recommendation systems, search ranking models or forecasting solutions.
• ML Platforms & Tooling: Exposure to ML platforms and tools such as Vertex AI, Databricks, SageMaker or Kubeflow.
• Streaming & Event Driven Systems: Experience with event driven architectures and streaming technologies such as Kafka or Pub/Sub.
• LLM Application Deployment: Experience deploying AI applications powered by LLMs and agent frameworks.

Why

• AI-Native Engineering: AI is deeply embedded in how our Engineering teams build and operate. We're moving beyond standalone LLM use into agentic workflows, AI-assisted development and automation integrated into real engineering systems and processes. Engineers have access to leading AI tools, hands-on training and the freedom to experiment with new ways of designing, building and shipping software.
• Our culture is unlike anywhere else and regardless of where you are in your career journey, we empower you to do your best work and have a big impact. Check us out & .
• Work with an incredible team to solve important challenges, helping to drive Australia and New Zealand's eCommerce future.
• Your role has a lot of ownership, autonomy and little red tape. You'll be empowered to achieve positive outcomes and your work will have a real impact.
• You'll be at the forefront of the eCommerce industry and be part of a company that are the Pioneers of eCommerce in Australia.
• Be an Intrepreneur, playing a hands on role in shaping our strategy at our HQ.
• Learning budget of $1000.
• A range of employee benefits such as; complimentary Kogan First Membership, team exclusive discounts, Health & Wellness program, Learning & Development and Lunch & Learns, Hackathons, Team member referral program, Company and team events and celebrations, community engagement (volunteering) and extensive career development opportunities plus loads more!

To find out more about how we work, our tech stack, processes and company culture, visit and