Machine Learning Engineer

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, , or 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 The Data Science & Machine Learning team is responsible for building models and APIs to help improve all of Penn Entertainments digital offerings. Our team values creativity, collaboration, ingenuity, and ownership. About the Work We’re focused on projects that directly improve user engagement and satisfaction. Some examples include : Personalized Recommendation Engines – Help users discover the games, promotions, and content they care about most through behavioral, contextual, and collaborative filtering models. Chat Toxicity Detection – Ensure a safe and welcoming community environment with NLP-based classification. Cross-sell Propensity Modeling – Predict which offerings resonate with each user and surface the right experience at the right time. Dynamic Personalization – Enable real‑time decision‑making in product features, content feeds, and user journeys using machine learning. You’ll also be instrumental in scaling our ML platform to support these future efforts. As part of the Machine Learning Engineering team, you will : Build and optimize end‑to‑end machine learning pipelines from data ingestion to deployment. Work closely with Product, Marketing, and Operations teams to align ML solutions with business goals. Improve our ML platform and deploy infrastructure using MLOps best practices. Evaluate and integrate new tools, models, and frameworks to enhance scalability and performance. Clearly communicate technical concepts to both technical and non‑technical stakeholders. Document your systems and workflows using Git, Confluence, and related tools. About You You’re someone who’s passionate about putting machine learning into production and making personalization work at scale. You bring : 3+ years of professional experience as a Machine Learning Engineer or in a similar role. A background in Computer Science, Data Science, Engineering, or a related technical field. Strong programming skills in Python and SQL. Bonus for Go, Rust, Scala, R, or C++. Experience with Docker, Kubernetes, Terraform, and scalable deployment tools. Hands‑on experience building CI / CD pipelines for ML systems. Proficiency in orchestration tools like Airflow, Kubeflow, or Dagster. Experience working on or contributing to dbt projects. Comfort working in cloud environments like AWS, GCP, or Azure. Familiarity with ML frameworks such as PyTorch, TensorFlow, Keras, or similar. Nice to have : Experience building real‑time personalization or recommendation systems at scale. Familiarity with virtual feature stores like Feast or Featureform. Exposure to working with or deploying large language models (LLMs) in production. What We Offer : Competitive compensation package Fun, relaxed work environment Education and conference reimbursements. Parental leave top up Opportunities for career progression and mentoring others #LI-REMOTE Candidates residing in Ontario requiring special accommodation can email #J-18808-Ljbffr