At a Glance
- Tasks: Build self-service ML tools and infrastructure for Data Scientists to deploy models independently.
- Company: Join Scientific Games, a leader in lottery games and innovative technology.
- Benefits: Enjoy competitive salary, remote work options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and career advancement.
- Why this job: Make a real impact in the exciting world of machine learning and gaming technology.
- Qualifications: Master's degree in a STEM field and 3+ years of ML engineering experience required.
The predicted salary is between 118800 - 145200 $ per year.
About 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.
Position Summary
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.
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.
Senior Machine Learning Engineer in Toronto employer: Scientific Games, LLC
Scientific Games is an exceptional employer located in Toronto, ON, offering a dynamic work culture that fosters innovation and collaboration. As a Senior Machine Learning Engineer, you'll have the opportunity to build cutting-edge ML platforms while enjoying a hybrid work model that promotes work-life balance. With a strong focus on employee growth, you will be part of a team that values your contributions and encourages continuous learning in a rapidly evolving industry.
