Job description

Our mission at Oura is to empower every person to own their inner potential. Our award-winning products help our global community gain a deeper knowledge of their readiness, activity, and sleep quality by using their Oura Ring and its connected app. We've helped millions of people understand and improve their health by providing daily insights and practical steps to inspire healthy lifestyles. Empowering the world starts with living our values and empowering our team. As a quickly growing company focused on helping people live healthier and happier lives, we ensure that our team members have what they need to do their best work — both in and out of the office. 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: Primary Focus – MLOps Platform and ML Enablement 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. Cross-functional Growth Areas 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. We would love to have you on our team if you have 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. What makes you stand out 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. Benefits At Oura, we care about you and your well-being. Everyone here at Oura has a ring of their own and we are continually looking to improve employee health. What we offer: Competitive salary and equity packages Health, dental, vision insurance, and mental health resources An Oura Ring of your own plus employee discounts for friends & family 20 days of paid time off plus 13 paid holidays plus 8 days of flexible wellness time off Paid sick leave and parental leave Oura takes a market-based approach to pay, which may vary depending on your location. US locations are categorized into tiers based on a cost of labor index for that geographic area. While most offers will be closer to the starting range, successful candidates' pay will be determined based on job-related skills, experience, qualifications, work location, internal peer equity, and market conditions. These ranges may be modified in the future. Region 1 $172,550 - $203,000 Region 2 $158,950 - $187,000 Region 3 $147,900 - $174,000 A recruiter can determine your zones/tiers based on your US location. We are not considering candidates residing in the following states: Alaska (AK), Delaware (DE), Iowa (IA), Mississippi (MS), Nebraska (NE), South Dakota (SD), West Virginia (WV), and Wisconsin (WI) Oura is proud to be an equal opportunity workplace. We celebrate diversity and are committed to creating an inclusive environment for all employees. Individuals seeking employment at Oura are considered without regard to age, ancestry, color, gender (including pregnancy, childbirth, or related medical conditions), gender identity or expression, genetic information, marital status, medical condition, mental or physical disability, national origin, protected family care or medical leave status, race, religion (including beliefs and practices or the absence thereof), sexual orientation, military or veteran status, or any other characteristic protected by federal, state, or local laws. We will not tolerate discrimination or harassment based on any of these characteristics. We will work to ensure individuals with disabilities are provided reasonable accommodation to participate in the interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Disclaimer: Beware of fake job offers! We’ve been alerted to scammers posing as ŌURA recruiters, especially for remote roles. Please note: Our jobs are listed only on the ŌURA Careers page and trusted job boards. We will never ask for personal information like ID or payment for equipment upfront. Official offers are sent through Docusign after a verbal offer, not via text or email. Stay cautious and protect your personal details. To all recruitment agencies: Oura does not accept agency resumes. Please do not forward resumes to our jobs alias, Oura employees, or any other organization's location. Oura is not responsible for any fees related to unsolicited resumes.

Requirements

Must-haves

This is a remote role in the US.
Primary Focus – MLOps Platform and ML Enablement
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.
Cross-functional Growth Areas
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.
We would love to have you on our team if you have
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.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

machine learningmlci/cdaws

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

Paid Time Off

Parental Leave

Wellness