Senior ML Engineer

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. This role plays a vital part in our mission to provide personalized and outstanding gaming experiences through sophisticated ML and AI solutions. If you are driven and passionate about employing technology to bring significant change, this opportunity is for you!

What You'll Do

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

What We're Looking For

• Strong software engineering expertise: Expert-level Python programming, software build, 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: Practical experience developing applications using LLMs, including timely input crafting, structured outputs, RAG, tool calling, and agentic workflows.
• ML/AI evaluation: Experience crafting evaluation pipelines, measuring model performance, identifying failure modes, and validating AI outputs.
• ML specialization: Solid hands-on experience within one or more fields including recommender systems, reinforcement learning, time-series modeling, unsupervised learning, or causal/Bayesian modeling.
• AI specialization: Background in at least one domain such as 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.
• Production ML: Experience with model deployment, inference services, monitoring, model versioning, and ML lifecycle management.
• Cloud & infrastructure: Solid understanding of cloud platforms (e.g., GCP, AWS, Azure), containers, CI/CD pipelines, and ML development environments.
• Performance engineering: Experience optimizing software performance, inference latency, or resource utilization.

If you’re passionate about using data science to build outstanding gaming experiences and ready to lead data products to success, we want to hear from you. Join Product Madness and help develop the future of mobile gaming!
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Important Information for Applicants**

• CV Language: Please submit your CV in English.
• Hybrid Working Policy: This role follows a hybrid working model, requiring attendance at our Barcelona office 3 days per week.

This job description may have been reviewed and enhanced using AI-assisted tools to improve clarity, consistency, and inclusivity. All final content, role requirements and hiring decisions remain subject to human review and approval by Aristocrat.

Compensation Philosophy

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.

- 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.