Senior MLOps Engineer

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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 pr

我们在Oura的使命是赋能每个人掌控自己的内在潜能。我们屡获殊荣的产品帮助全球社区通过使用Oura Ring及其连接应用，更深入地了解自己的准备状态、活动和睡眠质量。我们通过提供每日洞察和实用步骤来激励健康生活方式，已帮助数百万人理解并改善他们的健康。赋能世界始于践行我们的价值观并赋能我们的团队。作为一家快速成长、专注于帮助人们过上更健康、更快乐生活的公司，我们确保团队成员拥有做好最佳工作所需的一切——无论是在办公室内还是办公室外。我们正在寻找一位Senior MLOps Engineer加入我们的Data Engineering Analytics团队。在该职位中，您的主要重点将是领导平台、工作流和治理实践的设计与演进，使机器学习团队能够可靠地大规模开发、训练、部署和运营ML系统。您将与多个数据科学团队和业务领域合作，塑造强大的开发环境，推动模型生命周期改进，并帮助制定标准，以确保我们的ML系统可扩展、可维护且治理良好。该职位隶属于更广泛的共享云和数据平台生态系统，因此您还将与相邻的平台团队协作，并为实用的基础设施、部署和访问模式做出贡献，帮助DS团队更快推进。这是一个美国远程职位。您将做什么：主要重点——MLOps平台和ML赋能 领导支持Oura范围内机器学习工作流的环境、平台能力和运营基础的开发与改进。推动工作流和工具的设计与统一，以支持ML系统的可靠训练、编排和部署。与数据科学家和工程师合作，改进端到端ML生命周期，从实验和训练到部署和治理，同时让生产ML系统更易于管理和维护。定义并演进模型治理实践，包括可复现性、血缘、访问控制和运营标准。支持并帮助标准化ML工具和工作流，例如实验跟踪、模型打包和推广流程，其中MLflow或类似工具是技术栈的关键部分。与多个业务领域的团队协作，以接入新用例、优先推进平台改进，并提升共享ML能力的成熟度。识别并解决云中运行的ML基础设施的可靠性、可扩展性和成本效率问题。推动标准、自动化、基础设施即代码、CI/CD和文档建设，使ML开发在多个DS团队之间更轻松、更一致。跨职能成长领域 改进ML工作流及支持平台组件的基础设施自动化、CI/CD和可观测性。为支持大规模模型训练和批推理的工作流编排和平台集成做出贡献。与数据工程和平台团队合作，使ML系统与更广泛的数据平台和治理实践保持一致。帮助塑造团队在Oura范围内构建、交付和维护生产ML系统的最佳实践。如果您具备以下条件，我们非常希望您加入我们的团队：5年以上MLOps、机器学习工程、平台工程、数据工程或密切相关领域的经验。在AWS中运行生产工作负载的实操经验，并对云基础设施概念有深入理解。对机器学习生命周期有深入理解，包括训练工作流、跨ML系统的部署模式、监控和持续的模型维护。熟悉数据科学的工作方式以及支持实验和模型运营的工具，例如MLflow。熟悉工作流编排、基础设施即代码和CI/CD实

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