Staff Machine Learning Engineer

Company Overview

Scientific Games stands as a global front-runner in lottery gaming, sports betting, and advanced technology. Known for delivering innovative back-end systems, engaging entertainment experiences, and pioneering retail and digital solutions, the company is dedicated to elevating gaming daily. Their commitment includes advancing game design, leveraging data analytics, and leading in interactive lottery technologies. Rooted in trusted collaborations, Scientific Games combines cutting-edge innovation with consistent performance and stringent security to responsibly drive the global lottery sector forward.

Role Summary

The organization seeks a Staff Machine Learning Engineer to architect and develop its foundational machine learning platform. This role centers on creating an infrastructure that empowers Data Scientists to independently manage deployment, experimentation, batch scoring, online inference, monitoring, and controlled rollout processes.

Key Responsibilities

• Plan and define a multi-phase architectural roadmap for the inaugural ML platform.
• Develop self-service deployment frameworks enabling Data Scientists to independently move models to production.
• Design reusable components including model registry, deployment orchestration, feature retrieval, inference routing, system observability, and rollback mechanisms.
• Establish standardized workflows for batch inference, real-time serving, shadow deployment, canary release, A/B testing, and full-scale production rollout.
• Create engineering standards for SDKs, templates, continuous integration and deployment pipelines, testing practices, infrastructure as code, and developer workflows.
• Develop foundational platform elements to support recommendation systems, forecasting models, optimization tasks, and experimentation scenarios.
• Mentor Senior Machine Learning Engineers to improve software quality, architectural principles, and platform-centric approaches.
• Collaborate with Data Science leadership to ensure the platform enhances Data Scientist productivity without adding procedural pauses.

Required Qualifications

• Master's degree in Computer Science, Engineering, Distributed Systems, Machine Learning, or a related STEM discipline, or Bachelor's degree with notable platform engineering expertise.
• Over five years of practical experience in machine learning engineering, platform development, or managing large-scale production ML environments.
• Proven capability in designing platform architectures and reusable ML tooling methodologies.
• Experience in creating self-service internal platforms, developer tools, or ML deployment frameworks.
• Strong background in supporting Data Science teams via reusable infrastructure rather than centralized services.
• Leadership experience in defining architectural directions and mentoring engineers.

Technical Expertise

• Profound knowledge of machine learning system architectures covering batch and low-latency real-time serving.
• Hands-on experience with containerization (Docker), orchestration (Kubernetes), infrastructure automation, and cloud-native ML workloads.
• Expertise in model lifecycle tools such as MLFlow, including registries, validation procedures, and model promotion workflows.
• Advanced skills in designing CI/CD pipelines and deployment safety features like canary releases and rollback systems.
• Experience with feature stores, ensuring consistency between online/offline feature sets, and low-latency feature retrieval techniques.
• Strong Python programming skills for developing production-quality frameworks and SDKs.

Leadership & Collaboration

• Experience guiding technical direction for platform engineering teams.
• Proven mentorship capabilities supporting senior and mid-level machine learning engineers.
• Effective cross-functional collaboration with Data Science, data platform, and product engineering units.
• Preference for building scalable self-service systems maximizing organizational efficiency.

Preferred Experience

• Track record of developing ML platforms from inception to enterprise-wide deployment.
• Experience supporting recommendation, ranking, forecasting, and optimization model systems in a self-service context.
• Familiarity with cloud ML platforms such as Databricks, Azure ML, SageMaker, Vertex AI, or similar.
• Development of internal developer portals, command line interfaces, or workflow SDKs.
• A solid platform product mindset focusing on usability, adoption, and enhancing Data Science productivity.

Additional Information

The position will begin in a remote capacity and move to a hybrid arrangement; candidates must be residents of Toronto, Ontario, Canada.

Scientific Games is an Equal Opportunity Employer. The company does not discriminate based on race, color, sex, age, national origin, religion, sexual orientation, gender identity, veteran status, disability, or any other legally protected category.