Senior Machine Learning Engineer

SCIENTIFIC GAMES; Toronto; Hybrid; CAD 110,000 - 190,000; Full time

Job summary

Scientific Games is seeking a Senior Machine Learning Engineer to help build the foundations of our ML platform from the ground up. This role emphasizes self-service tooling and golden paths that enable Data Scientists to move models from experimentation to production across batch and real-time use cases.

You will collaborate with Staff MLEs, Data Scientists, and platform stakeholders to establish reusable infrastructure, deployment workflows, observability standards, and developer experience

Qualifications

• 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.

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.
• 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.

Skills

Python programming

Software engineering fundamentals

Collaboration with Data Scientists

Developer experience

A/B testing tooling

Education

Master’s degree in Computer Science, Engineering, Machine Learning, Software Engineering, or related STEM field

Bachelor’s degree in related STEM field with strong equivalent industry depth

Tools

PyTorch

TensorFlow

Docker

Kubernetes

GitHub Actions

MLflow

Model registry

Databricks

PySpark

Airflow

Job description

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.

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 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

Education

Required Qualifications

• 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

SG is an Equal Opportunity Employer and does not discriminate against applicants due to race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class. If you’d like more information about your equal employment opportunity rights as an applicant under the law, please click here for EEOC Poster.