Senior Machine Learning Engineer
Sglottery Open · verified Oct 22, 2026 At a glance
Senior Machine Learning Engineering role at Sglottery. Toronto · full-time · no salary stated · we estimate CA$148,625–CA$191,400.
Sglottery has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Create shared tools that let data scientists package models and run batch predictions or live inference
- Build workflows for model registration, environment changes, feature access and safe release reversals
- Automate training checks and staged releases, from validation through shadow and canary deployment
- Define monitoring for model condition, response speed, feature freshness, data quality and business measures
- Shape the platform’s first architecture with Staff MLEs and establish reusable developer workflows
What you bring
- At least three years working in ML engineering, platform engineering or production ML systems
- A related STEM master’s, or a related STEM bachelor’s plus substantial equivalent industry experience
- Strong Python skills and deployment experience with PyTorch, TensorFlow, Docker and Kubernetes
- Experience delivering production models for batch and real-time use and taking data science work into production
Who this fits
This role fits a senior engineer focused on reusable ML infrastructure and self-service deployment for data scientists. You must live in Toronto, Ontario. The job begins remotely and later becomes hybrid.
From the employer
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.