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 Staff Machine Learning Engineer to define and build the machine learning platform architecture for the organization.
This is a platform creation role, not a platform operations gatekeeper role.
Define the target architecture and phased roadmap for the first ML platform
Build self-service deployment frameworks so Data Scientists can productionize models independently
Architect reusable capabilities for model registry, deployment, feature retrieval, and rollback
Define golden paths for batch, real-time, canary, A/B and full production releases
Mentor Senior MLEs and raise engineering quality across the team
We are looking for a Staff Machine Learning Engineer to define and build the machine learning platform architecture for the organization. This team will create the enabling layer that allows Data Scientists to self-serve deployment, experimentation, batch scoring, online inference, monitoring, and safe rollout workflows.
This is a platform creation role, not a platform operations gatekeeper role. The success metric is not how many deployments the team executes directly, but how effectively the platform allows domain Data Scientists to deploy independently through highly reliable self-service workflows. The initial Staff MLE hires will establish the architectural foundations, engineering standards, reusable tooling strategy, and platform roadmap that the Senior MLE team will scale.
Define the target architecture and phased roadmap for the organization’s first ML platform
Build self-service deployment frameworks enabling Data Scientists to productionize models independently
Architect reusable capabilities for model registry, deployment orchestration, feature retrieval, inference routing, observability, and rollback
Define golden paths for batch inference, real-time serving, shadow deployment, canary rollout, A/B testing, and full production release
Mentor Senior MLEs and raise software engineering quality, architecture rigor, and platform thinking across the team
Master’s degree in Computer Science, Engineering, Distributed Systems, Machine Learning, or another related STEM field
5+ years of hands-on experience in ML engineering, platform engineering, or large-scale production ML systems
Deep expertise in ML systems architecture across batch and low-latency real-time serving
Demonstrated ability to define technical direction for platform teams
Bachelor’s degree with exceptional relevant platform engineering depth is acceptable
Proven experience designing platform architecture and reusable ML tooling standards
Experience building greenfield ML platforms from zero to scaled enterprise adoption
Experience supporting self-service recommendation, ranking, forecasting, and optimization systems
Familiarity with Databricks, Azure ML, SageMaker, Vertex AI, or equivalent ML platforms
Experience building internal developer portals, CLIs, or workflow SDKs
Education: - Master’s degree in Computer Science, Engineering, Distributed Systems, Machine Learning, or another related STEM field - Bachelor’s degree with exceptional relevant platform engineering depth is acceptable
- Master’s degree in Computer Science, Engineering, Distributed Systems, Machine Learning, or another related STEM field - Bachelor’s degree with exceptional relevant platform engineering depth is acceptable Experience:
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- Experience with feature stores, online/offline feature parity, and low-latency feature retrieval - Strong Python engineering standards and ability to write production-grade frameworks and SDKs Leadership:
- Experience supporting self-service recommendation, ranking, forecasting, and optimization systems - Familiarity with Databricks, Azure ML, SageMaker, Vertex AI, or equivalent ML platforms - Experience building internal developer portals, CLIs, or workflow SDKs
- Deep expertise in ML systems architecture across batch and low-latency real-time serving - Strong hands-on experience with Docker, Kubernetes, infrastructure automation, and cloud-native ML workloads - Strong expertise in model lifecycle tooling including MLFlow, registries, validation gates, and promotion workflows
- Strong hands-on experience with Docker, Kubernetes, infrastructure automation, and cloud-native ML workloads - Strong expertise in model lifecycle tooling including MLFlow, registries, validation gates, and promotion workflows - Advanced experience designing CI/CD, canary, rollback, and deployment safety systems for ML
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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 Staff Machine Learning Engineer to define and build the machine learning platform architecture for the organization. This team will create the enabling layer that allows Data Scientists to self-serve deployment, experimentation, batch scoring, online inference, monitoring, and safe rollout workflows.
This is a platform creation role, not a platform operations gatekeeper role. The success metric is not how many deployments the team executes directly, but how effectively the platform allows domain Data Scientists to deploy independently through highly reliable self-service workflows. The initial Staff MLE hires will establish the architectural foundations, engineering standards, reusable tooling strategy, and platform roadmap that the Senior MLE team will scale.
**This position will start remotely and transition to a hybrid role. Candidates must be local to Toronto, ON.
Key Responsibilities
Define the target architecture and phased roadmap for the organization’s first ML platform
Build self-service deployment frameworks enabling Data Scientists to productionize models independently
Architect reusable capabilities for model registry, deployment orchestration, feature retrieval, inference routing, observability, and rollback
Define golden paths for batch inference, real-time serving, shadow deployment, canary rollout, A/B testing, and full production release
Establish platform engineering standards across SDKs, templates, CI/CD, testing, infrastructure-as-code, and developer workflows
Design platform primitives that support recommendation systems, forecasting, optimization, and experimentation use cases
Mentor Senior MLEs and raise software engineering quality, architecture rigor, and platform thinking across the team
Partner with Data Science leadership to ensure the platform accelerates DS velocity rather than introducing process friction
Required Qualifications
Education:
Master’s degree in Computer Science, Engineering, Distributed Systems, Machine Learning, or another related STEM field
Bachelor’s degree with exceptional relevant platform engineering depth is acceptable
Experience:
5+ years of hands-on experience in ML engineering, platform engineering, or large-scale production ML systems
Proven experience designing platform architecture and reusable ML tooling standards
Experience building self-service internal platforms, developer tooling, or ML deployment frameworks
Strong experience enabling applied Data Science teams through reusable infrastructure rather than centralized service models
Experience leading architecture decisions and mentoring engineers
Technical Skills:
Deep expertise in ML systems architecture across batch and low-latency real-time serving
Strong hands-on experience with Docker, Kubernetes, infrastructure automation, and cloud-native ML workloads
Strong expertise in model lifecycle tooling including MLFlow, registries, validation gates, and promotion workflows
Advanced experience designing CI/CD, canary, rollback, and deployment safety systems for ML
Experience with feature stores, online/offline feature parity, and low-latency feature retrieval
Strong Python engineering standards and ability to write production-grade frameworks and SDKs
Leadership:
Demonstrated ability to define technical direction for platform teams
Strong mentorship track record for Senior and mid-level MLEs
Strong cross-functional influence with DS, data platform, and product engineering teams
Bias toward building self-service systems that maximize organizational leverage
Preferred Qualifications:
Experience building greenfield ML platforms from zero to scaled enterprise adoption
Experience supporting self-service recommendation, ranking, forecasting, and optimization systems
Familiarity with Databricks, Azure ML, SageMaker, Vertex AI, or equivalent ML platforms
Experience building internal developer portals, CLIs, or workflow SDKs
Strong platform product thinking focused on usability, adoption, and DS productivit
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
This is a platform creation role, not a platform operations gatekeeper role. The success metric is not how many deployments the team executes directly, but how effectively the platform allows domain Data Scientists to deploy independently through highly reliable self-service workflows. The initial Staff MLE hires will establish the architectural foundations, engineering standards, reusable tooling strategy, and platform roadmap that the Senior MLE team will scale. **This position will start remotely and transition to a hybrid role. Candidates must be local to Toronto, ON. Qualifications
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