Staff Machine Learning Engineer
Scientific Games Corporation19 days ago
Toronto, CanadaStaff+
Responsibilities
- Define the target architecture and phased roadmap for the organization’s first ML platform.
- Build self-service deployment frameworks that enable Data Scientists to productionize models independently.
- Architect reusable capabilities for model registries, deployment orchestration, feature retrieval, inference routing, observability, and rollback.
- Establish workflows for batch inference, real-time serving, shadow deployment, canary rollout, A/B testing, and full production release.
- Set platform engineering standards across SDKs, templates, testing, infrastructure automation, and developer workflows.
- Design platform primitives supporting recommendation systems, forecasting, optimization, and experimentation.
- Mentor Senior MLEs and improve software engineering quality, architecture rigor, and platform thinking.
- Partner with Data Science leadership and cross-functional teams to maximize self-service ML productivity.
Requirements
- Master’s degree in Computer Science, Engineering, Distributed Systems, Machine Learning, or a related STEM field, or a bachelor’s degree with exceptional relevant platform engineering depth.
- At least 5 years of hands-on experience in ML engineering, platform engineering, or large-scale production ML systems.
- Experience designing platform architecture, reusable ML tooling standards, self-service internal platforms, developer tooling, or ML deployment frameworks.
- Experience enabling applied Data Science teams through reusable infrastructure and leading architecture decisions.
- 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.
- Expertise with model lifecycle tooling, registries, validation gates, and promotion workflows, including MLFlow.
- Advanced experience designing deployment safety systems involving CI/CD, canary releases, and rollback.
- Experience with feature stores, online/offline feature parity, and low-latency feature retrieval.
- Strong Python engineering skills and ability to write production-grade frameworks and SDKs.
- Demonstrated technical direction, mentorship, and cross-functional influence.
- Preferred experience building greenfield ML platforms, supporting recommendation/ranking/forecasting/optimization systems, and creating internal developer portals, CLIs, or workflow SDKs.
- Preferred familiarity with Databricks, Azure ML, SageMaker, Vertex AI, or equivalent ML platforms.
Benefits
- The position starts remotely and transitions to a hybrid role.
- Candidates must be local to Toronto, Ontario.
- Scientific Games is an equal opportunity employer.
Tech Stack
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