Master's degree in Computer Science, Engineering, Distributed Systems, Machine Learning, or a related STEM field
Bachelor's degree with significant platform engineering experience accepted
5+ years of hands-on experience in ML engineering or platform engineering
Proven experience in designing platform architecture and reusable ML tooling
Experience with self-service internal platforms and developer tooling
Strong expertise in Docker, Kubernetes, and cloud-native ML workloads
Demonstrated ability to mentor engineers and influence technical direction
Responsibilities
Define the overall architecture and phased roadmap for the ML platform
Build frameworks enabling Data Scientists to independently deploy models
Architect reusable capabilities for model management and observability
Establish best practices for batch inference and production release strategies
Design engineering standards across CI/CD and development workflows
Mentor and elevate the software engineering quality within the team
Collaborate with Data Science leadership to enhance data science implementation speed
Benefits
Mentorship opportunities to develop leadership skills
Work in a pioneering role driving innovation in ML platform design
Collaborative culture with cross-functional teams
Focus on self-service systems that enhance organizational efficiency
Opportunity to influence the engineering standards of the future
Full Job Description
Position Summary
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 role is based out of Toronto.
Qualifications
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 productivit
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.
