Staff Machine Learning Engineer

About Scientific Games

Scientific Games stands as a global frontrunner in lottery games, sports betting, and advanced technology, serving as the preferred partner for government lotteries worldwide. The company specializes in sophisticated backend systems, engaging entertainment platforms, and pioneering retail and digital solutions that enhance the experience of play on a daily basis. With a focus on pushing game designs forward and leading in data analytics and iLottery, Scientific Games is built on trusted partnerships, relentless innovation, exemplary performance, and robust security, driving the global lottery industry ahead responsibly.

Position Overview

The Staff Machine Learning Engineer will be instrumental in designing and developing the machine learning platform architecture for the organization. This role involves creating a supportive foundation permitting Data Scientists to autonomously deploy, experiment, perform batch scoring, conduct online inference, monitor models, and manage safe rollout processes.

This role focuses on crafting the platform rather than operating it. Success will be measured by how effectively the platform empowers Data Scientists to independently deploy their models through reliable self-service workflows. As the initial Staff Machine Learning Engineer hire, you will lay down architectural foundations, define engineering standards, develop reusable tooling strategies, and map out the platform's roadmap for the Senior MLE team to expand upon.

Work Arrangement

The role will commence remotely and eventually transition to a hybrid working model. Applicants must reside locally in Toronto, Ontario.

Key Responsibilities

• Establish the target architecture and phased development plan for the company's inaugural ML platform.
• Create self-service deployment frameworks that empower Data Scientists to independently move models into production.
• Design reusable components for model registry, deployment orchestration, feature retrieval, inference routing, monitoring, and rollback capabilities.
• Develop best practices for batch inference, real-time serving, shadow deployments, canary releases, A/B testing, and full production launches.
• Implement engineering standards across software development kits, templates, continuous integration/delivery pipelines, testing, infrastructure-as-code, and developer workflows.
• Design foundational platform elements that support use cases such as recommendation systems, forecasting, optimization, and experimentation.
• Provide mentorship to Senior Machine Learning Engineers, elevating software engineering quality, architectural discipline, and platform-oriented thinking within the team.
• Collaborate closely with Data Science leadership to ensure the platform enhances Data Scientist efficiency without imposing process burdens.

Qualifications

• Master’s degree in Computer Science, Engineering, Distributed Systems, Machine Learning, or related STEM disciplines; alternatively, a Bachelor’s degree coupled with exceptional experience in platform engineering is acceptable.
• Over 5 years of practical experience in machine learning engineering, platform engineering, or large-scale production ML systems.
• Demonstrated expertise in designing platform architecture and establishing reusable ML tooling standards.
• Strong background in developing self-service internal platforms, developer tools, or ML deployment frameworks.
• Proven ability to support applied Data Science teams through reusable infrastructure rather than centralized services.
• Experience leading architectural decision-making and mentoring engineering teams.

Technical Expertise

• In-depth knowledge of ML systems architecture, including batch workflows and low-latency real-time serving.
• Proficiency with Docker, Kubernetes, infrastructure automation, and cloud-native ML workload management.
• Expertise in model lifecycle management tools such as MLFlow, registries, validation mechanisms, and promotion workflows.
• Advanced experience designing CI/CD pipelines, canary deployments, rollback procedures, and safeguards for ML releases.
• Familiarity with feature stores, ensuring consistency between online and offline features, and fast feature retrieval.
• Strong Python programming skills with the ability to develop production-quality frameworks and SDKs.

Leadership and Collaboration

• Proven skill in setting technical direction for platform-focused teams.
• Successful history mentoring Senior and mid-level Machine Learning Engineers.
• Strong ability to influence cross-functional teams including Data Science, data platform teams, and product engineering.
• Preference for building self-service platforms that maximize organizational scalability and leverage.

Preferred Qualifications

• Experience launching innovative ML platforms from inception through enterprise-wide adoption.
• Knowledge of supporting self-service recommendation, ranking, forecasting, and optimization ML systems.
• Familiarity with cloud ML platforms such as Databricks, Azure ML, SageMaker, or Vertex AI.
• Experience developing internal developer portals, command-line interfaces, or workflow SDKs.
• Strong product-oriented approach in platform design focusing on usability, adoption, and boosting Data Scientist productivity.

Equal Opportunity Statement

Scientific Games is committed to equal employment opportunity and does not discriminate based on race, color, sex, age, national origin, religion, sexual orientation, gender identity, veteran status, disability, or other protected categories under federal, state, or local laws.