Staff Machine Learning Engineer in Toronto

Scientific Games is the global leader in lottery games, sports betting and technology, and the partner of choice for government lotteries. 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 organization’s 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: Master’s degree in Computer Science, Engineering, Distributed Systems, Machine Learning, or another related STEM field. Bachelor’s 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 productivity.