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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.<br><br>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 ESPN BET, Hollywood Casino, theScore Bet Sportsbook & Casino, 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.<br><br>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.<br><br>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.<br><br>The Machine Learning Engineering team is responsible for building, deploying, and optimizing models and APIs to help improve all of Penn Entertainments digital offerings. Our team values creativity, collaboration, ingenuity, and ownership. As a Software Engineer, ML Platform you'll be instrumental in crafting the next generation of user experiences. You will design, build, and deploy sophisticated machine learning models and infrastructure that directly impact how users discover content, engage with our community, and explore the full spectrum of Penn Entertainment's offerings. This role offers a unique chance to contribute to high-impact projects while helping to advance our cutting-edge ML platform.<br><br><strong>About The Work <br><br></strong>We're focused on projects that directly improve user engagement and satisfaction. Some examples include:<br><br><ul><li>Personalized Recommendation Engines: Connect users with the content, games, and promotions they'll love. </li><li>Experimentation Framework: Guide data-driven decision-making by providing foundations for AB testing and experimentation. </li><li>Dynamic Personalization: Implement real-time, ML-driven decisions to create seamless user journeys. </li><li>Cutting-Edge MLOps: Help us scale our ML platform using the latest tools and best practices (GCP, Kubernetes, PyTorch, Dagster, and more). <br><br></li></ul>As part of the Machine Learning Engineering team, you will:<br><br><ul><li>Build and optimize end-to-end machine learning pipelines from data ingestion to deployment. </li><li>Work closely with Product, Marketing, and Operations teams to align ML solutions with business goals. </li><li>Improve our ML platform and deploy infrastructure using MLOps best practices. </li><li>Evaluate and integrate new tools, models, and frameworks to enhance scalability and performance. </li><li>Clearly communicate technical concepts to both technical and non-technical stakeholders. </li><li>Document your systems and workflows using Git, Confluence, and related tools. <br><br></li></ul><strong>About You<br><br></strong>You’re someone who’s passionate about putting machine learning into production and making personalization work at scale. You bring:<br><br><ul><li>Experience: 3+ years of experience in ML, data, or backend software engineering</li><li>Technical Skills: Proficiency in Python and SQL. Familiarity with cloud platforms such as GCP, AWS, or Azure. </li><li>MLOps & Infrastructure: Hands-on experience with ML model deployment, CI/CD pipelines, IaC tools (Terraform), containerization (Docker, Kubernetes), and orchestration tools (Dagster, Airflow, Kubeflow, or similar). </li><li>ML Tooling: Experience with model packaging and serving technologies such as TensorFlow, Pytorch, MLflow, Vertex AI, or AWS SageMaker. </li><li>Collaboration: Solid communication skills and a desire to work cross-functionally with data scientists, ML engineers, and platform teams. </li><li>Education: Bachelor’s degree in Computer Science, Engineering, or a related technical field. <br><br></li></ul><strong>Nice to have:<br><br></strong><ul><li>Experience building real-time personalization or recommendation systems at scale. </li><li>Familiarity with monitoring, observability, and alerting tools for ML systems. </li><li>Exposure to working with or deploying large language models (LLMs) in production. <br><br></li></ul><strong>What We Offer</strong> :<br><br><ul><li>Competitive compensation package </li><li>Fun, relaxed work environment </li><li>Education and conference reimbursements. </li><li>Opportunities for career progression and mentoring others <br><br></li></ul>Salary Range<br><br>$143,000—$180,400 USD<br><br><em>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.<br><br></em>
