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About This Role
PENN Entertainment, Inc. is North America's leading provider of integrated entertainment, sports content, and casino gaming experiences. From casinos and racetracks to online gaming, sports betting and entertainment content, we deliver the experiences people want, how and where they want them.
We're always on the lookout for those who are passionate about creating and delivering cutting\-edge online gaming and sports media products. Whether it's through Hollywood Casino, theScore Bet Sportsbook, or theScore media app, we're excited to push the boundaries of what's possible. These state\-of\-the\-art platforms are powered by proprietary in\-house technology, a key component of PENN's omnichannel gaming and entertainment strategy.
When you join PENN Entertainment's digital team, you'll not only work on these cutting\-edge platforms through theScore and PENN Interactive, but you'll also be part of a company that truly cares about your career growth. We're committed to supporting you as you expand your skills and explore new opportunities.
With locations throughout North America, you can build a future at PENN Entertainment wherever you are. If you want to challenge conventions in gaming, media and entertainment, we want to talk to you.
About the Role \& Team
We're looking for an CX Manager, AI Optimization to lead the evolution of our customer support experience through AI, automation, knowledge management, and platform optimization. In this role, you'll own the strategy and execution of our support technologies—including Zendesk and AI\-powered tools—while identifying opportunities to streamline operations, improve self\-service, and empower both customers and agents. Working cross\-functionally with Product, Operations, and other business partners, you'll drive scalable solutions, optimize support workflows, and ensure our knowledge ecosystem evolves alongside our products and customer needs.
About the Work
- Lead the strategy, implementation, and ongoing optimization of Customer Support AI and automation initiatives, including chatbot and agent\-assist (CoPilot) functionality, to drive improved customer outcomes, operational efficiency, and self\-service adoption.
- Administer and optimize the Zendesk Support platform, including workflows, automations, integrations, permissions, and system enhancements.
- Analyze customer journeys, support workflows, internal tools, and product experiences to identify improvement opportunities and implement scalable solutions that enhance both customer and agent experiences.
- Lead the monitoring of performance metrics and user feedback to evaluate the effectiveness of AI, knowledge management, and support operations, using insights to drive continuous improvement.
- Support and guide team members contributing to AI bot improvement and optimization on their tasks and priorities.
- Own the vision and continuous evolution of Operations knowledge management, including internal knowledge bases and external Help Centers, ensuring content remains accurate, accessible, and aligned with evolving products and policies.
- Partner with cross\-functional stakeholders to develop, maintain, and govern Standard Operating Procedures (SOPs), knowledge assets, and training materials that support operational excellence.
- Serve as a subject matter expert and advocate for AI adoption, knowledge management, and support platform best practices across the Customer Experience organization.
About You
- 4\+ years of experience in Customer Experience, Customer Support Operations, CX Systems, Product Management, or a related field.
- 2\+ years of experience administering and optimizing customer support platforms, including configuring workflows, automations, and support technologies.
- Experience implementing or managing AI\-powered customer support solutions, including chatbots, agent\-assist tools, workflow automation, and knowledge recommendation systems, with a working knowledge of Large Language Models (LLMs) and their application in customer support.
- Proven ability to lead cross\-functional initiatives, manage competing priorities, and influence stakeholders while delivering scalable operational improvements.
- Strong analytical, technical, and problem\-solving skills, with experience leveraging customer feedback, operational metrics, and reporting to identify opportunities and drive measurable improvements.
- Demonstrated experience creating and maintaining operational documentation, including Standard Operating Procedures (SOPs), internal knowledge bases, customer\-facing Help Centers, and training materials.
- Proficiency with customer support technologies, documentation systems, reporting tools, and project management platforms within a B2C environment.
- Experience with leading others in a Customer Service environment
*Nice To Have*
- Experience with Zendesk AI (Ultimate AI and CoPilot), Salesforce AgentForce, AWS Connect, or similar AI\-enabled customer support technologies.
- Experience with conversational design, chatbot administration, customer journey mapping, or product management methodologies.
- Experience evaluating and implementing third\-party CX technologies, managing vendor relationships, and/or working within highly regulated industries such as gaming or financial services.
What We Offer
- Competitive compensation package
- Fun, relaxed work environment
- Education and conference reimbursements.
\#LI\-REMOTE
*Penn Interactive is proud to be an equal opportunity workplace. We will consider all qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability, veteran status, genetic information, or any other basis protected by applicable law.Base pay is one part of the Total Rewards that Penn Interactive provides to compensate and recognize employees for their work. Most sales positions are eligible for a Commission under the terms of an applicable plan, while most non\-sales positions are eligible for a Bonus. Additionally, Penn Interactive provides best\-in\-class benefits to eligible employees. We believe that benefits should connect you to the support you need when it matters most, and should help you care for those who matter most. That's why we provide an array of options, expert guidance and always\-on tools, that are personalized to meet the needs of your reality – to help support you physically, financially and emotionally through the big milestones and in your everyday life.*
Salary Context
This $85K-$95K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Penn Interactive Ventures, 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 Required
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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($90K) sits 59% below the category median. Disclosed range: $85K to $95K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Penn Interactive Ventures AI Hiring
Penn Interactive Ventures has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $95K - $95K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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