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Scientific Games:
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Scientific Games is the global leader in lottery games, sports betting and technology, and the partner of choice for government lotteries. From cutting\-edge backend systems to exciting entertainment experiences and trailblazing retail and digital solutions, we elevate play every day. We push game designs to the next level and are pioneers in data analytics and iLottery. Built on a foundation of trusted partnerships, Scientific Games combines relentless innovation, legendary performance, and unwavering security to responsibly propel the global lottery industry ever forward.
Position Summary
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Scientific Games is looking for a senior hands\-on leader to build and run a centralized AI\-enabled process re\-engineering capability.
This leader will help SG improve important work across a focused portfolio of high\-value domains. Early areas may include RFP processing, IT / DevOps / integration, software engineering, game art and design, data science, and shared service operations. The role starts with business outcomes and process diagnosis, then moves into workflow redesign, AI\-enabled pilots, evaluation, adoption, and scale.
The right person will be strong at understanding how work actually happens. They will be able to sit with teams, map the value chain, identify bottlenecks, clarify decision points, and redesign workflows. They will also understand modern AI well enough to decide when to use retrieval, drafting, summarization, orchestration, review support, simulation, or bounded agentic execution.
This is a senior leadership role with hands\-on expectations. The person should be able to operate as a high\-impact individual contributor, build a small team, coordinate domain sponsors, direct TPM support, manage external partners where useful, and create reusable methods for SG.
Success will be measured by improved business outcomes, stronger workflows, reusable methods, safer AI adoption, and measurable gains in selected high\-value processes.
Core Responsibilities
- Build SG’s method for AI\-enabled process re\-engineering.
- Select and shape high\-value opportunities with executive sponsors.
- Lead current\-state diagnostics, constraint analysis, and target workflow design.
- Determine where AI should support retrieval, drafting, orchestration, review, decision support, or bounded execution.
- Lead controlled pilots with baselines, evaluation plans, human review models, and rollout criteria.
- Create reusable templates, governance patterns, architecture patterns, and playbooks.
- Partner with leaders across product, engineering, IT, data science, creative, sales, finance, legal, and operations.
- Select and manage external partners where they accelerate the work or add specialized capability.
- Communicate progress, risks, decisions, and results clearly to senior leadership.
Experience that Fits
The role should require substantial experience across process improvement, operations, product, engineering, consulting, AI transformation, or enterprise technology. A reasonable target is 12\+ years, with evidence of leading cross\-functional change and delivering measurable results.
The person should understand value\-stream thinking, bottleneck analysis, workflow redesign, operating model change, and adoption. They should be credible with modern AI concepts including LLMs, retrieval, agentic workflows, orchestration, human\-in\-the\-loop design, evaluation, observability, and guardrails. They do not need to be the deepest engineer in the room, but they must be technical enough to challenge architecture, vendor claims, and implementation plans.
They should be able to write clearly. This matters because the work will involve ambiguous problems, cross\-functional decisions, and senior stakeholders. A leader who cannot turn complexity into clear language will struggle to create alignment.
Qualifications
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Candidate Profile
The strongest candidate will probably come from one of several backgrounds.
One possible profile is a senior process transformation leader who has modern AI depth. This person may have led operational excellence, business process re\-engineering, product operations, or enterprise transformation work and has learned how to apply LLMs, agents, workflow automation, and AI evaluation in real systems.
Another possible profile is an AI product / solution leader with deep business process instincts. This person may have built AI\-enabled workflows, internal platforms, or agentic systems and has enough operating experience to avoid tool\-first thinking.
A third possible profile is a senior consultant or former consultant who has led practical transformation work and can operate inside a company with accountability for outcomes. This person should be willing to own results, not only recommendations.
Across backgrounds, the required traits are the same: clear thinking, curiosity about work, strong writing, practical AI fluency, process discipline, executive influence, comfort with ambiguity, and the ability to turn messy problems into operating plans.
First\-Year Success Profile
By the end of the first year, the right leader should have established SG’s method for AI\-enabled process re\-engineering, completed serious diagnostics in the first\-wave domains, launched controlled pilots with baselines and evaluation plans, delivered at least one measurable improvement in a high\-value workflow, and created reusable templates and governance patterns.
The best evidence of success will be that SG leaders start bringing better\-framed problems to the team. Instead of asking for a bot, they should begin asking where the constraint sits, how the workflow should change, and what role AI should play.
Physical Requirements
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. While performing the duties of this job, the employee is regularly required to sit, stand, walk, bend, use hands, operate a computer, and have specific vision abilities to include close and distance vision, and ability to adjust focus working with computer and business equipment.
Work Conditions
Scientific Games, LLC and its affiliates (collectively, “SG”) are engaged in highly regulated gaming and lottery businesses. As a result, certain SG employees may, among other things, be required to obtain a gaming or other license(s), undergo background investigations or security checks, or meet certain standards dictated by law, regulation or contracts. In order to ensure SG complies with its regulatory and contractual commitments, as a condition to hiring and continuing to employ its employees, SG requires all of its employees to meet those requirements that are necessary to fulfill their individual roles. As a prerequisite to employment with SG (to the extent permitted by law), you shall be asked to consent to SG conducting a due diligence/background investigation on you.
This job description should not be interpreted as all\-inclusive; it is intended to identify major responsibilities and requirements of the job. The employee in this position may be requested to perform other job\-related tasks and responsibilities than those stated above.
SG is an Equal Opportunity Employer and does not discriminate against applicants due to race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. If you’d like more information about your equal employment opportunity rights as an applicant under the law, please click here for EEOC Poster .
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Scientific Games, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Scientific Games AI Hiring
Scientific Games has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Alpharetta, GA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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