Master's degree in Computer Science or related STEM field; Bachelor's degree with strong industry experience accepted
3+ years of experience in machine learning engineering or platform engineering
Proven experience building production batch and real-time ML systems
Strong Python programming and software engineering fundamentals
Hands-on experience with deployment workflows in PyTorch and TensorFlow
Experience with Docker, Kubernetes, and cloud-native deployment patterns
Strong understanding of CI/CD workflows and API-based inference services
Responsibilities
Build reusable self-service tooling for model deployment and inference
Develop capabilities for Data Scientists to deploy and monitor models independently
Design CI/CD pipelines for automated machine learning workflows
Establish golden-path templates and SDKs for standardized ML delivery
Contribute to observability standards for model health and performance
Partner with Staff MLEs to define the architecture of the ML platform
Full Job Description
Position Summary
About the Role
We are looking for a Senior Machine Learning Engineer to help build the foundations of our machine learning platform from the ground up. This role is not about creating a centralized gatekeeping team. Instead, the mission is to build self-service ML tooling and golden paths that enable Data Scientists to independently take models from experimentation to reliable production deployment across batch and real-time use cases. You will partner closely with Staff MLEs, Data Scientists, and platform stakeholders to establish the first generation of reusable ML infrastructure, deployment workflows, observability standards, and developer experience patterns that scale across the organization
**This position will start remotely and transition to a hybrid role. Candidates must be local to Toronto, ON.
Qualifications
Key Responsibilities
Build reusable self-service tooling for model packaging, deployment, batch inference, and real-time serving
Develop platform capabilities that enable Data Scientists to independently deploy, monitor, and iterate on their own models in production Build foundational ML workflows including model registry, environment promotion, rollback, feature access patterns, and inference APIs
Design CI/CD pipelines for automated training, validation, shadow deployment, canary rollout, rollback, and full production promotion workflows
Establish golden-path templates, SDKs, CLIs, and reference implementations to standardize ML system delivery
Contribute to observability standards across model health, latency, feature freshness, data quality, and business KPI monitoring
Partner with Staff MLEs to shape the first-generation architecture of the ML platform
Required Qualifications
Education:
Master's degree in Computer Science, Engineering, Machine Learning, Software Engineering, or another related STEM field
Bachelor's degree in a related STEM field with strong equivalent industry depth is also acceptable
Experience:
3+ years of hands-on experience in ML engineering, platform engineering, or production ML systems
Proven experience building production batch and real-time ML systems • Experience working closely with Data Scientists to productionize models and experimentation workflows
Strong experience building reusable tooling, frameworks, or internal developer platforms
Technical Skills:
Strong Python and software engineering fundamentals
Hands-on experience with PyTorch and TensorFlow model deployment workflows
Experience with Docker, Kubernetes, and cloud-native deployment patterns
Strong CI/CD experience using GitHub Actions and cloud-native CI/CD workflows
Experience with MLflow, model registry workflows, and multi-environment promotion
Strong understanding of API-based inference services, async batch scoring, and event-driven pipelines
Soft Skills:
Strong collaboration with Data Scientists and product engineering teams
Builder mindset with focus on developer experience and adoption
Ability to translate infrastructure complexity into simple self-service workflows
Preferred Qualifications:
Experience building internal ML platforms from zero to first scaled adoption
Experience with feature stores and reusable feature access SDKs
Familiarity with Databricks, PySpark, Airflow, or equivalent orchestration tooling
Experience with self-service experimentation and A/B testing tooling
Experience designing platform abstractions that maximize DS autonomy without compromising reliability
About Scientific Games Corporation
Light & Wonder, Inc., formerly Scientific Games Corporation, is an American corporation that provides gambling products and services. The company is headquartered in Las Vegas, Nevada, with lottery headquarters and production plant in Alpharetta, Georgia.
Light & Wonder's gaming division provides products such as slot machines, table games, shuffling machines, and casino management systems. Its brands include Bally, WMS, and Shuffle Master.
