Interested in this AI/ML Engineer role at Aristocrat?
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At Aristocrat, we are passionate about bringing happiness to life through the power of play. As a world leader in gaming content and technology, we strive to build outstanding experiences for our customers and players. The Vice President, Data Science defines and delivers Aristocrat's enterprise Data Science strategy and builds a Data Science organization that delivers business impact through machine learning, statistical modeling, advanced analytics, and AI. As a member of the Enterprise Data \& Analytics leadership team, this role partners with senior business and technology leaders to shape strategy, accelerate innovation, and enable data\-driven decision making across the enterprise.
What You'll Do
- Define and implement the enterprise Data Science strategy, roadmap, and operating model.
- Serve as the executive advisor on Data Science strategy, investments, risks, and emerging opportunities.
- Partner with executive leaders to identify, prioritize, and scale Data Science, machine learning, and AI opportunities that solve business problems, improve decision making, and create measurable business value and competitive advantage.
- Evaluate emerging Data Science, AI, and machine learning technologies, tools, and techniques, and establish a roadmap for adoption where they provide relevant business value.
- Lead, develop and grow a high\-performing organization through multiple levels of leadership, including Directors, Managers, Data Scientists, and Machine Learning Engineers.
- Establish enterprise standards, governance, and operating principles for machine learning, statistical modeling, experimentation, optimization, decision science, and responsible AI.
- Define the enterprise strategy and architecture for model lifecycle management, including model development, deployment, monitoring, governance, and continuous improvement.
- Collaborate with Architecture, Data Governance, Platform Engineering, and Analytics teams to ensure scalable and balanced delivery of Data Science capabilities.
- Drive enterprise adoption of advanced analytics and machine learning solutions that improve business outcomes and decision making.
- Establish frameworks to measure adoption, business impact, and value realization of Data Science investments.
- Shape an organization known for technical excellence, innovation, accountability, and continuous learning.
What We're Looking For
- 15\+ years of progressive leadership experience in Data Science, Machine Learning, AI, Advanced Analytics, Statistical Modeling, or related quantitative disciplines.
- Deep familiarity with gaming, digital product, media, or entertainment businesses preferred.
- Experience leading enterprise Data Science organizations through Directors, Managers, and senior technical leaders.
- Proven success building and scaling Data Science capabilities that enable production machine learning, AI, and advanced analytics solutions.
- Experience defining enterprise Data Science operating models, governance frameworks, and model lifecycle practices that support the scalable adoption of advanced analytics, machine learning, and AI.
- Experience evaluating emerging Data Science, AI, and machine learning technologies and converting innovation opportunities into practical business capabilities.
- Outstanding executive communication, partner influence, and organizational leadership skills, able to build strong partnerships across business and technology functions.
- Master's degree or PhD in Data Science, Statistics, Computer Science, Mathematics, Engineering, Economics, or a related quantitative field preferred; equivalent experience will also be considered.
Why Aristocrat?
Aristocrat is a world leader in gaming content and technology, and a top\-tier publisher of free\-to\-play mobile games. We deliver great performance for our B2B customers and bring joy to the lives of the millions of people who love to play our casino and mobile games. And while we focus on fun, we never forget our responsibilities. We strive to lead the way in responsible gameplay, and to lift the bar in company governance, employee wellbeing and sustainability. We’re a diverse business united by shared values and an inspiring mission to bring joy to life through the power of play.
We aim to create an environment where individual differences are valued, and all employees have the opportunity to realize their potential. We welcome and encourage applications from all people regardless of age, gender, race, ethnicity, cultural background, disability status or LGBTQ\+ identity. EEO M/F/D/V
- World Leader in Gaming Entertainment
- Robust benefits package
- Global career opportunities
Our Values
- All about the Player
- Talent Unleashed
- Collective Brilliance
- Good Business Good Citizen
Travel Expectations
Up to 25%Pay Range
$189,000 \- $351,000 per year
Our goal is to pay a market competitive salary focusing near the median of our pay ranges. However, final offers for all positions will be based on several factors such as experience level, education, skills, work location, and internal pay equity.
This position offers a comprehensive benefits package, including health, dental, and vision insurance, paid time off, and a 401(k) plan with employer matching, more details available at https://atibenefits.com/.
Additional Information
*At this time, we are unable to sponsor work visas for this position. Candidates must be authorized to work in the job posting location for this position on a full\-time basis without the need for current or future visa sponsorship.*
Salary Context
This $189K-$351K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Aristocrat, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. This role's midpoint ($270K) sits 26% above the category median. Disclosed range: $189K to $351K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Aristocrat AI Hiring
Aristocrat has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Las Vegas, NV, US. Compensation range: $351K - $351K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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