ML engineer in mobile gaming

Product Madness owns the mobile gaming industry by developing engaging, ground-breaking gaming experiences. We value data’s role in improving player experiences and guiding strategic business decisions. Join our innovative, collaborative team and contribute to crafting the future of mobile gaming. As a Senior ML Engineer at Aristocrat, you will work with modern technology that transforms the gaming experience for millions of players.

• Build, implement, train, evaluate, and optimize machine learning models for prediction, recommendation, personalization, and decision support.
• Construct and blend LLM-powered applications, retrieval-augmented generation (RAG) systems, and agentic workflows into internal tools and business processes.
• Translate ML/AI requirements into reliable software components, services, and APIs, ensuring maintainability, performance, and scalability.
• Formulate evaluation techniques, validate model quality, pinpoint failure modes, and uphold robustness, safety, and reliability of ML/AI outputs.
• Work alongside MLOps Engineers to deploy, monitor, and maintain ML/AI solutions, ensuring smooth integration with existing platforms and applications.
• Increase model inference speed, resource management, latency reduction, and scalability of ML/AI applications.
• Write clean, well-tested, maintainable Python code; implement automated testing, code reviews, documentation, and reliable error handling.
• Retrieve, integrate, validate, and prepare data from multiple sources to support model training, inference, and evaluation.
• Work closely with Data Scientists, MLOps Engineers, Data Engineers, and product partners to transform business needs into practical data-driven intelligence solutions.
• Participate in architecture discussions, mentor less experienced engineers, and help improve development standards and engineering practices.

• Expert-level Python programming, software build, automated testing, debugging, version control, and collaborative development practices.
• Hands-on experience developing, training, evaluating, and optimizing supervised learning models using frameworks such as scikit-learn, PyTorch, TensorFlow, or similar.
• Practical experience developing applications using LLMs, including timely input crafting, structured outputs, RAG, tool calling, and agentic workflows.
• Experience crafting evaluation pipelines, measuring model performance, identifying failure modes, and validating AI outputs.
• Solid hands-on experience within one or more fields including recommender systems, reinforcement learning, time-series modeling, unsupervised learning, or causal/Bayesian modeling.
• Background in at least one domain such as LLM/NLP applications, agentic workflows, or computer vision.
• Strong SQL skills, experience integrating data through APIs and other interfaces, and understanding of data quality and preprocessing.
• Experience with model deployment, inference services, monitoring, model versioning, and ML lifecycle management.
• Solid understanding of cloud platforms (e.g., GCP, AWS, Azure), containers, CI/CD pipelines, and ML development environments.
• Experience optimizing software performance, inference latency, or resource utilization.

• This role follows a hybrid working model, requiring attendance at our Barcelona office 3 days per week.
• We offer a comprehensive pay and benefits package designed to stay competitive in the market, support your wellbeing, and recognise your contribution to our success.
• Our approach is underpinned by a pay-for-performance belief in rewarding individual impact.
• Your specific compensation package will be determined by 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 wellbeing benefits, paid time off, retirement plans, insurance coverage, and other local or statutory benefits.
• Specific details about compensation and benefits for this position will be discussed during the recruitment process.