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. We are looking for a Senior Machine Learning Engineer to help build the foundations of our machine learning platform from the ground up. Instead, the mission is to build self‑service ML tooling and golden paths that enable Data Scientists to independently take models from experimentation to reliable production deployment across batch and real‑time use cases.

You will partner closely with Staff MLEs, Data Scientists, and platform stakeholders to establish the first generation of reusable ML infrastructure, deployment workflows, observability standards, and developer experience patterns that scale across the organization Build reusable self‑service tooling for model packaging, deployment, batch inference, and real‑time serving Develop platform capabilities that enable Data Scientists to independently deploy, monitor, and iterate on their own models in production Build foundational ML workflows including model registry, environment promotion, rollback, feature access patterns, and inference APIs Design CI/CD pipelines for automated training, validation, shadow deployment, canary rollout, rollback, and full production promotion workflows Establish golden‑path templates, SDKs, CLIs,



and reference implementations to standardize ML system delivery Contribute to observability standards across model health, latency, feature freshness, data quality, and business KPI monitoring Partner with Staff MLEs to shape the first‑generation architecture of the ML platform Master’s degree in Computer Science, Engineering, Machine Learning, Software Engineering, or another related STEM field Bachelor’s degree in a related STEM field with strong equivalent industry depth is also acceptable 3+ years of hands‑on experience in ML engineering, platform engineering, or production ML systems ~ Proven experience building production batch and real‑time ML systems ~ Experience working closely with Data Scientists to productionize models and experimentation workflows ~ Robust experience building reusable tooling, frameworks, or internal developer platforms Technical Skills Strong Python and software engineering fundamentals Hands‑on experience with PyTorch and TensorFlow model deployment workflows Experience with Docker, Kubernetes, and cloud‑native deployment patterns Strong understanding of API‑based inference services, async batch scoring, and event‑driven pipelines Solid collaboration with Data Scientists and product engineering teams Experience building internal ML platforms from zero to first scaled adoption Familiarity with Databricks, PySpark, Airflow, or equivalent orchestration tooling Experience with self‑service experimentation and A/B testing tooling 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. #

📌 Machine Learning Engineer- Cloud Deployment (Remote) (Montreal)
🏢 Scientific Games
📍 Montreal