ML engineer in mobile games

Product Madness is a leading company in the mobile gaming industry, dedicated to developing innovative and engaging gaming experiences. We value the role of data in improving the player experience and guiding strategic business decisions. Join our innovative and collaborative team and help build the future of mobile gaming. As a Senior ML Engineer at Aristocrat, you will work with advanced technologies that transform the gaming experience for millions of users. This role plays a fundamental part in our mission to deliver personalized and exceptional gaming experiences through sophisticated machine learning and artificial intelligence solutions. If you are passionate about technology and motivated to use it to make a meaningful impact, this opportunity is for you!

• Develop production machine learning solutions: Create, implement, train, evaluate, and optimize machine learning models for prediction, recommendation, personalization, and decision support.
• Develop AI applications: Create and integrate applications based on large language models (LLMs), retrieval-augmented generation (RAG) systems, and agentic workflows into internal tools and business processes.
• Design end-to-end solutions: Transform ML/AI requirements into reliable software components, services, and APIs, ensuring their maintainability, performance, and scalability.
• Evaluate ML and AI systems: Design evaluation methodologies, validate model quality, identify potential failures, and ensure the robustness, security, and reliability of ML/AI-generated results.
• Integrate models into production: Collaborate with MLOps engineers to deploy, monitor, and maintain ML/AI solutions, ensuring seamless integration with existing platforms and applications.
• Optimize performance: Improve model inference speed, resource management, latency reduction, and the scalability of ML/AI applications.
• Apply good engineering practices: Write clean, well-tested, and maintainable Python code; implement automated tests, participate in code reviews, maintain technical documentation, and ensure proper error handling.
• Work with complex data: Obtain, integrate, validate, and prepare data from multiple sources to support model training, inference, and evaluation.
• Collaborate with different teams: Work closely with data scientists, MLOps engineers, data engineers, and product teams to turn business needs into practical, intelligent, data-driven solutions.
• Contribute to technical excellence: Participate in architecture discussions, mentor less experienced engineers, and help improve development standards and engineering practices.

• Strong software engineering experience: Advanced proficiency in Python, software design and development, automated testing, debugging, version control, and collaborative development practices.
• Machine learning: Hands-on experience developing, training, evaluating, and optimizing supervised learning models using frameworks such as scikit-learn, PyTorch, TensorFlow, or similar.
• Generative AI: Hands-on experience developing LLM-based applications, including prompt engineering, structured outputs, RAG, tool calling, and agentic workflows.
• ML/AI evaluation: Experience designing evaluation pipelines, measuring model performance, identifying potential failures, and validating AI-generated results.
• ML specialization: Strong hands-on experience in one or more areas, such as recommender systems, reinforcement learning, time series modeling, unsupervised learning, or causal and Bayesian modeling.
• AI specialization: Experience in at least one of the following areas: LLM/NLP applications, agentic workflows, or computer vision.
• Data engineering fundamentals: Strong SQL skills, experience integrating data through APIs and other interfaces, and understanding of data quality and preprocessing processes.
• Machine learning in production: Experience with model deployment, inference services, monitoring, model versioning, and ML lifecycle management.
• Cloud and infrastructure: Strong knowledge of cloud platforms such as GCP, AWS, or Azure, containers, CI/CD pipelines, and machine learning development environments.
• Performance optimization: Experience optimizing software performance, reducing inference latency, or improving resource utilization.

• We offer a comprehensive compensation and benefits package designed to remain competitive in the market, support your well-being, and recognize your contribution to our success. Our approach is based on a pay-for-performance philosophy that rewards individual impact. Your specific compensation package will be determined based on factors such as your skills, experience, qualifications, and location.
• Depending on your role and location, you may be eligible for annual bonuses and incentives, health and wellness benefits, paid time off, retirement plans, insurance coverage, and other local benefits or benefits established by applicable law.
• Specific details about compensation and benefits for this position will be discussed during the selection process.