Senior MLOps Engineer

We are looking for a Senior MLOps Engineer to join our Data Engineering & Analytics team.

In this role, your primary focus will be leading the design and evolution of the platforms, workflows, and governance practices that enable machine learning teams to develop, train, deploy, and operate ML systems reliably at scale. You will work across multiple data science teams and business domains, shaping a strong development environment, driving model lifecycle improvements, and helping set the standards that keep our ML systems scalable, maintainable, and well governed. The role sits within a broader shared cloud and data platform ecosystem, so you will also collaborate with adjacent platform teams and contribute to practical infrastructure, deployment, and access patterns that help DS teams move faster.

This is a remote role in the US.

What you will do:

• Lead the development and improvement of the environment, platform capabilities, and operational foundations that support machine learning workflows across Oura.
• Drive the design and unification of workflows and tooling that support reliable training, orchestration, and deployment of ML systems.
• Partner with data scientists and engineers to improve the end-to-end ML lifecycle, from experimentation and training through deployment and governance, while making production ML systems easier to manage and maintain.
• Define and evolve model governance practices, including reproducibility, lineage, access controls, and operational standards.
• Support and help standardize ML tooling and workflows such as experiment tracking, model packaging, and promotion processes, with MLflow or similar tooling as a key part of the stack.
• Collaborate with teams across multiple business domains to onboard new use cases, prioritize platform improvements, and raise the maturity of shared ML capabilities.
• Identify and resolve reliability, scalability, and cost-efficiency issues in ML infrastructure running in the cloud.
• Drive standards, automation, infrastructure-as-code, CI/CD, and documentation that make ML development easier and more consistent across multiple DS teams.

• Improve infrastructure automation, CI/CD, and observability for ML workflows and supporting platform components.
• Contribute to workflow orchestration and platform integrations that support model training and batch inference at scale.
• Partner with data engineering and platform teams to align ML systems with broader data platform and governance practices.
• Help shape best practices for how teams build, ship, and maintain production ML systems across Oura.

• 5+ years of experience in MLOps, machine learning engineering, platform engineering, data engineering, or a closely related field.
• Hands-on experience running production workloads in AWS and a strong understanding of cloud infrastructure concepts.
• Strong understanding of the machine learning lifecycle, including training workflows, deployment patterns across ML systems, monitoring, and ongoing model maintenance.
• Familiarity with data science ways of working and the tooling that supports experimentation and model operations, such as MLflow.
• Familiarity with workflow orchestration, infrastructure-as-code, and CI/CD practices for ML or data platforms.
• Familiarity with secure access patterns, governance controls, and shared cloud or data platform services that support ML work at scale.
• Experience building or supporting production-grade ML workflows with a focus on reliability, reproducibility, and maintainability.
• Proven ability to drive standards and improvements across multiple teams and business domains.
• Strong communication and collaboration skills, with the ability to work effectively with both technical and non-technical stakeholders.
• Comfort operating in a distributed team with a high degree of ownership and autonomy.

• Experience supporting multiple data science teams through shared MLOps platforms or tooling.
• Prior experience with Databricks.
• Experience with governance and operational controls for ML systems at scale.
• Experience driving cross-team tooling or workflow standardization in ML or data platforms.
• Experience working in a high-growth environment with multiple stakeholders and evolving ML platform needs.