Scientific Games Corporation

Senior Machine Learning Engineer

Scientific Games Corporation • $110K — $130K *
Toronto, ON M3C 0E3In-Person
Enterprise Technology
Less than 5 years of experience
Today
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Job Overview by Ladders

Qualifications

  • 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.
Learn more about Scientific Games Corporation
Size
9,500 employees
Market Cap
$5.6 billion
Industry
Information Technology
Net Income
-$569 million
Founded
1973
5 Year Trend
-5.7%
Revenue
$2.7 billion
NASDAQ
SGMS
* Ladders Estimates

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