Toronto, Canada
Live, first seen by us 77 days agoEmployment type in the employer’s posting: Full timeFrom the employer’s posting
We are looking for a Senior Machine Learning Engineer to help build the foundations of our machine learning platform from the ground up.
**This position will start remotely and transition to a hybrid role.
Build reusable self-service tooling for model packaging, deployment, and serving
Design CI/CD pipelines for training, validation, canary rollout, and rollback
Establish golden-path templates, SDKs, CLIs, and reference implementations
Contribute to observability standards for model health, latency, and data quality
Partner with Staff MLEs to shape the first-generation ML platform architecture
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.
Build reusable self-service tooling for model packaging, deployment, batch inference, and real-time serving
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
Master’s degree in Computer Science, Engineering, Machine Learning, Software Engineering, or another related STEM field
3+ years of hands-on experience in ML engineering, platform engineering, or production ML systems
Strong Python and software engineering fundamentals
Experience with MLflow, model registry workflows, and multi-environment promotion
Ability to translate infrastructure complexity into simple self-service workflows
Bachelor’s degree in a related STEM field with strong equivalent industry depth is also acceptable
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
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
- 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:
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Technical Skills: - Strong Python and software engineering fundamentals - Hands-on experience with PyTorch and TensorFlow model deployment workflows
- 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
- 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
- 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
- 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
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New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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.
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.
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.
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Toronto, Canada
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
No clear work-authorization passage found. Eligibility is unconfirmed.