Senior Machine Learning Engineer

Job summary

You will build reusable self-service tooling and platform capabilities to enable Data Scientists to independently deploy and monitor models in production. Additionally, you will design CI/CD pipelines and establish observability standards to ensure reliable ML system delivery across the organization.

Job details

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 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 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.

What you’ll do

You will build reusable self-service tooling and platform capabilities to enable Data Scientists to independently deploy and monitor models in production. Additionally, you will design CI/CD pipelines and establish observability standards to ensure reliable ML system delivery across the organization.

Requirements

The role requires a Bachelor's or Master's degree in a STEM field and at least 3 years of hands-on experience in ML or platform engineering. Candidates must possess strong software engineering fundamentals and experience with production-grade ML systems, including model deployment and infrastructure workflows.

Listed skills

• Kubernetes · Preferred
• CI/CD · Preferred
• Docker · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• Machine Learning Engineering
• PyTorch
• TensorFlow
• Docker
• Kubernetes
• CI/CD
• GitHub Actions
• MLflow
• API Development
• Data Science
• Platform Engineering
• Cloud-native deployment
• Model Registry
• Feature Stores
• System Architecture
• Cloud-Native Computing
• Pipelines
• Observability
• Workflow Management
• Apache Airflow
• A/B Testing
• Abstractions
• Application Programming Interface (API)
• Data Analysis
• Computer Science
• Data Quality
• Event-Driven Programming
• Github
• Innovation
• Python (Programming Language)
• Key Performance Indicators (KPIs)
• Machine Learning
• Product Family Engineering
• Product Engineering
• Self Service Technologies
• Software Engineering
• Tooling
• Collaboration
• Reliability
• PySpark
• Databricks
• PyTorch (Machine Learning Library)
• Docker (Software)
• Streaming Machine Learning
• Machine Learning Infrastructure

Job areas

• Technology
• Software
• Data & Analytics
• Engineering
• Machine Learning Engineer
• Software Developers
• Computer and Information Research Scientists