Senior Machine Learning Systems Engineer

Build and operate the internal machine learning platform that powers AI products across a large health technology organization serving post-acute and long-term care providers. The role spans ML platform architecture, MLOps tooling, and production reliability, partnering closely with product and engineering teams to translate ML needs into reusable capabilities. It also contributes to safe, scalable AI/ML and LLM-based solutions used by tens of thousands of provider organizations.

• Collaborate with product and engineering teams to translate ML needs into reliable, reusable platform capabilities
• Design and build scalable data and ML pipelines covering training, evaluation, deployment, and serving
• Develop MLOps tooling and workflows, including CI/CD for models, a model registry, feature stores, and experiment tracking
• Ensure production reliability through monitoring, alerting, and automated remediation
• Implement security mechanisms such as authentication, role-based access control, audit logging, and compliance monitoring
• Integrate the platform securely with existing systems, APIs, and data sources while optimizing cost, performance, and scale
• Mentor engineers and promote reusable platform patterns and best practices

• Expert-level proficiency in Python and Java with strong software engineering fundamentals
• Hands‑on experience designing and building ML platforms and MLOps workflows, with familiarity in tools such as MLflow, Kubeflow, Ray, and model‑serving frameworks
• Cloud platform experience, primarily Azure, with secondary familiarity in AWS and GCP
• Experience with containerization and orchestration of ML runtimes using Docker and Kubernetes

• Bachelor's degree or higher in Computer Science, Machine Learning, or a related field
• Working familiarity with Azure Machine Learning components and Databricks processing and serverless environments
• Experience implementing security at scale, including RBAC, multi‑factor authentication, network security, and compliance monitoring
• Experience optimizing large model training and inference, including LLM serving, for performance and cost

• Remote role with periodic travel to Mississauga and/or Salt Lake City offices for onboarding, team events, and semi‑annual and annual meetings
• Founder‑led, privately held company that reinvests a meaningful share of revenue into research and development