Staff Machine Learning Engineer

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.

About the Role

We are looking for a Staff Machine Learning Engineer to define and build the machine learning platform architecture for the organization. This team will create the enabling layer that allows Data Scientists to self-serve deployment experimentation batch scoring online inference monitoring and safe rollout workflows.

This is a platform creation role not a platform operations gatekeeper role. The success metric is not how many deployments the team executes directly but how effectively the platform allows domain Data Scientists to deploy independently through highly reliable self-service workflows. The initial Staff MLE hires will establish the architectural foundations engineering standards reusable tooling strategy and platform roadmap that the Senior MLE team will scale.

**This position will start remotely and transition to a hybrid role. Candidates must be local to Toronto ON.

Key Responsibilities

• Define the target architecture and phased roadmap for the organizations first ML platform

• Build self-service deployment frameworks enabling Data Scientists to productionize models independently

• Architect reusable capabilities for model registry deployment orchestration feature retrieval inference routing observability and rollback

• Define golden paths for batch inference real-time serving shadow deployment canary rollout A/B testing and full production release

• Establish platform engineering standards across SDKs templates CI/CD testing infrastructure-as-code and developer workflows

• Design platform primitives that support recommendation systems forecasting optimization and experimentation use cases

• Mentor Senior MLEs and raise software engineering quality architecture rigor and platform thinking across the team

• Partner with Data Science leadership to ensure the platform accelerates DS velocity rather than introducing process friction

Required Qualifications

Education:

• Masters degree in Computer Science Engineering Distributed Systems Machine Learning or another related STEM field

• Bachelors degree with exceptional relevant platform engineering depth is acceptable

Experience:

• 5 years of hands-on experience in ML engineering platform engineering or large-scale production ML systems

• Proven experience designing platform architecture and reusable ML tooling standards

• Experience building self-service internal platforms developer tooling or ML deployment frameworks

• Strong experience enabling applied Data Science teams through reusable infrastructure rather than centralized service models

• Experience leading architecture decisions and mentoring engineers

Technical Skills:

• Deep expertise in ML systems architecture across batch and low-latency real-time serving

• Strong hands-on experience with Docker Kubernetes infrastructure automation and cloud-native ML workloads

• Strong expertise in model lifecycle tooling including MLFlow registries validation gates and promotion workflows

• Advanced experience designing CI/CD canary rollback and deployment safety systems for ML

• Experience with feature stores online/offline feature parity and low-latency feature retrieval

• Strong Python engineering standards and ability to write production-grade frameworks and SDKs

Leadership:

• Demonstrated ability to define technical direction for platform teams

• Strong mentorship track record for Senior and mid-level MLEs

• Strong cross-functional influence with DS data platform and product engineering teams

• Bias toward building self-service systems that maximize organizational leverage

Preferred Qualifications:

• Experience building greenfield ML platforms from zero to scaled enterprise adoption

• Experience supporting self-service recommendation ranking forecasting and optimization systems

• Familiarity with Databricks Azure ML SageMaker Vertex AI or equivalent ML platforms

• Experience building internal developer portals CLIs or workflow SDKs

• Strong platform product thinking focused on usability adoption and DS productivit

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. If youd like more information about your equal employment opportunity rights as an applicant under the law please click here for EEOC Poster.