Senior 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 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 role is based out of Toronto.

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