Junior Machine Learning Engineer

Who We Are

Entain Australia & New Zealand is fearlessly transforming the racing, sports, and entertainment landscape - reimagining how customers experience their favourite brands.

As part of a global powerhouse operating in over 40 countries with nearly 30,000 people, we’re home to leading ANZ names like Ladbrokes, Neds, TAB, and Betcha.

Across Entain ANZ, we’re shaping the future of sport and gaming entertainment through creative innovation, storytelling, and technology - creating experiences that connect with millions of fans.

Your Impact

Our Data Science and Engineering teams are focused on building intelligent, data-driven products that are shaping the future of gaming and wagering in Australia - delivering personalised, reliable, and responsible experiences for our customers.

With millions of customers interacting with our platforms, the machine learning solutions you help build will operate at scale—where performance, reliability, and real-world impact truly matter.

At Entain, we are a product-led organisation, with a strong focus on using data and AI to enhance customer experiences and drive better business outcomes.

Machine Learning Engineering plays a key role in that - supporting teams to move models into production, improving how we build and operate ML systems, and ensuring our solutions are scalable, reliable, and well-governed across a wide range of use cases.

A Day in the Life

• Contribute to building and improving ML pipelines, from data preparation to deployment
• Support the deployment and monitoring of models in production environments
• Collaborate with Data Scientists and Engineers to translate ideas into scalable solutions
• Assist in automating workflows across the ML lifecycle (training, evaluation, retraining)
• Troubleshoot model or pipeline issues and help identify root causes
• Contribute to internal tools, APIs, and reusable ML components
• Participate in code reviews and team ceremonies in an Agile environment
• Learn and apply best practices in MLOps, cloud, and production engineering

Your Strengths

• Foundational experience in machine learning, software engineering, or data engineering
• Working knowledge of Python and SQL, with an interest in writing clean, maintainable code
• Understanding of the ML lifecycle, from experimentation to production
• Familiarity with cloud platforms or strong willingness to learn (AWS, GCP, Azure, Databricks)
• Exposure to tools like Git, Docker, or CI/CD pipelines
• Curious mindset with strong problem-solving ability
• Comfortable collaborating with cross-functional teams
• Strong communication skills and eagerness to learn from others

Life at Entain

At Entain, it’s more than a job - it’s a place to grow, connect, and make an impact. We value individuality, celebrate collaboration, and create space for bold ideas that move people. Here, you’ll help shape the voice of some of Australia and New Zealand’s most recognised brands - while developing your own craft in a supportive, ambitious team.

Perks You’ll Love

Hybrid & Flexible: Balance your life and work.

Recognition & Rewards: Celebrate wins and enjoy retailer discounts.

Wellbeing Perks: Support for your health and happiness.

Entain, there’s nothing like it.

For more information, please reach out to us at careers@entaingroup.com.au or hit apply now.

Visit website

At Entain Australia & New Zealand, the customer is the center of everything we do. Through our global scale and flagship brands, we leverage our technology and data insights to create products and experiences which bring moments of excitement into people’s lives.

To do this, we invest in the best talent to deliver our ambitions. Our vision is to be the world leader in sports wagering, gaming, and venue entertainment. By creating exciting and trusted experiences for our customers, we are revolutionising the wagering and entertainment space as we go.

We’re experts in putting the customer first, with a firm commitment to ensure safer gambling and moments of excitement flow from our market-leading approach to responsibility.