[All remote jobs](https://jobicy.com/jobs.md)[![ŌURA logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2026/07/011c99434cd6-221.webp)](https://jobicy.com/company/oura.md)[ŌURA](https://jobicy.com/company/oura.md) # Senior MLOps Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/oura.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 147,900–203k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished1 Nov 2026Apply before1 Dec 2026Listing views31Application actions2Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Oura is seeking a Senior MLOps Engineer to build and evolve the shared platform, workflows, and governance practices supporting production machine-learning systems. The role leads improvements across the ML lifecycle, including experimentation, training, deployment, monitoring, reproducibility, model lineage, and access controls. It requires strong AWS infrastructure experience plus practical knowledge of MLflow, orchestration, infrastructure-as-code, CI/CD, and cloud reliability. The engineer will partner closely with data science, data engineering, and platform teams while establishing standards across multiple business domains. This is a senior, autonomous remote US position with significant technical and cross-functional influence. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis senior role combines production ML platform engineering, cloud infrastructure, governance, reliability, and cross-team technical leadership. Success requires making durable architectural decisions while standardizing workflows for several data science teams. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$175,450US market range (per year)$155k–$210k0$231.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses US location-tiered annual base-pay ranges of $147,900 to $203,000 USD. The combined midpoint is $175,450 per year; a reasonable estimated US market range for a senior MLOps engineer is $155,000 to $210,000 annually. ## Core skills Skills and capabilities most closely associated with this opportunity. [MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[Machine learning lifecycle](https://jobicy.com/jobs?search_keywords=Machine%20learning%20lifecycle.md)[MLflow](https://jobicy.com/jobs?search_keywords=MLflow.md)[Model deployment](https://jobicy.com/jobs?search_keywords=Model%20deployment.md)[Model governance](https://jobicy.com/jobs?search_keywords=Model%20governance.md)[Workflow orchestration](https://jobicy.com/jobs?search_keywords=Workflow%20orchestration.md)[Infrastructure as Code](https://jobicy.com/jobs?search_keywords=Infrastructure%20as%20Code.md)[CI/CD](https://jobicy.com/jobs?search_keywords=CICD.md)[Cloud infrastructure](https://jobicy.com/jobs?search_keywords=Cloud%20infrastructure.md)[Data engineering](https://jobicy.com/jobs?search_keywords=Data%20engineering.md)[Observability](https://jobicy.com/jobs?search_keywords=Observability.md)[Model monitoring](https://jobicy.com/jobs?search_keywords=Model%20monitoring.md)[Access controls](https://jobicy.com/jobs?search_keywords=Access%20controls.md)[Databricks](https://jobicy.com/jobs?search_keywords=Databricks.md) Sample interview questionsHow would you design an MLOps platform that supports multiple data science teams with different use cases?I would begin by identifying common lifecycle needs, such as experiment tracking, artifact storage, reproducible training, model registration, deployment, monitoring, and access control. I would provide these as secure, self-service platform capabilities with documented standards and reusable templates, while allowing controlled extension points for team-specific requirements. Platform adoption and reliability metrics would guide iterative improvements. How have you improved reproducibility and governance for production ML workloads? I would version datasets, code, environments, model artifacts, and configuration; capture lineage in an experiment and model registry; and implement approval gates for model promotion. Role-based access controls, auditable deployment records, and documented rollback procedures help ensure that production models can be traced, reviewed, and safely maintained. What would you monitor after deploying a machine-learning model? I would monitor service reliability indicators such as latency, errors, throughput, and infrastructure utilization alongside model indicators such as prediction distributions, data drift, feature quality, model performance where labels are available, and business outcome metrics. Alerts should have clear ownership and runbooks so teams can respond quickly to incidents or degradation. Describe how you would implement CI/CD for an ML training and deployment workflow. I would use automated checks for code quality, tests, security, and infrastructure changes, then execute reproducible training pipelines in a controlled environment. Validated artifacts would be registered with metadata and evaluated against predefined promotion criteria before deployment through staged environments. The release process would include rollback support, deployment observability, and approval controls appropriate to model risk. How would you balance standardization with the needs of rapidly evolving data science teams? I would standardize the high-value foundations such as security, lineage, deployment interfaces, CI/CD, and monitoring while keeping experimentation workflows flexible. I would engage representatives from data science teams early, publish clear platform contracts, and prioritize improvements based on adoption friction and business impact. This approach creates consistency without forcing every use case into an unnecessarily rigid workflow. Opportunity details ## About this role. Our mission at ŌURA 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](https://ouraring.com/about-us) 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) ŌURA 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 ŌURA 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: ŌURA does not accept agency resumes. Please do not forward resumes to our jobs alias, ŌURA employees, or any other organization’s location. ŌURA is not responsible for any fees related to unsolicited resumes. 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