Staff Machine Learning Engineer

Scientific Games:

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.

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