Aristocrat

Senior ML Engineer

Barcelona, Spain

Actively hiring, first seen by us today

Pay: Pay not listed
Work setting: Not stated
Employment: Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting

What you’ll work on

Full posting

Build and optimize ML models for prediction, recommendation and personalization
Construct LLM-powered applications, RAG systems and agentic workflows for internal tools
Deploy, monitor and maintain ML/AI solutions with MLOps Engineers
Design evaluation methods and find failure modes in ML and AI outputs
Improve inference speed, latency and scalability of ML/AI applications

From the employer’s posting

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.

Work alongside MLOps Engineers to deploy, monitor, and maintain ML/AI solutions

Formulate evaluation techniques, validate model quality, pinpoint failure modes

Increase model inference speed, resource management, latency reduction, and scalability of ML/AI applications.

Apply at Aristocrat

Posting record

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Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Nov 3, 2026today
Recorded sightings: 2
Last seen by us: Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 0

Early. Similar jobs usually come down between day 6 and day 69.

From 4,284 similar postings that came down. Coming down is not always a hire.

Aristocrat is hiring

14 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Takes jobs down at a usual pace

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
SQL
AWS
Azure
GCP
PyTorch
scikit-learn
TensorFlow

Where the posting names them

- 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.

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.

- 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.

- 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.

- 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.

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About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025: $140,300
Expected change, 2025 to 2035: 21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

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!

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.

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