Sglottery
Staff Machine Learning Engineer
Montreal, Canada · Staff+
Sponsorship not specifiedDetected 5 days ago
PythonDistributed SystemsDatabricksAzureDockerKubernetesCI/CDPlatform EngineeringMachine LearningData AnalysisData ScienceMLOpsA/B TestingForecastingLeadershipMentoring
About the role
- 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.
Requirements
- Bachelor's degree with exceptional relevant platform engineering depth is acceptable
- 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 leading architecture decisions and mentoring engineers
- 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
Nice to have
- Experience supporting self-service recommendation, ranking, forecasting, and optimization systems
- Familiarity with Databricks, Azure ML, SageMaker, Vertex AI, or equivalent ML platforms
- Strong platform product thinking focused on usability, adoption, and DS productivit
- If you'd like more information about your equal employment opportunity rights as an applicant under the law, please click here for EEOC Poster.
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
- 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
Benefits
- We are looking for a Staff Machine Learning Engineer to define and build the machine learning platform architecture for the organization.
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This listing is sourced directly from Sglottery's careers page and normalized into a canonical job model.