Ōura

San Diego
Total Offices: 3
850 Total Employees
Year Founded: 2013

Ōura Benefits Overview

Compensation + Benefits

Offers generous parental leave

Offers health insurance

Work-Life Balance + Wellbeing

Offers generous PTO

Provides paid holidays

Company Culture

Offers a remote work program

Provides free snacks and drinks

Recently posted jobs

9 Hours AgoSaved
Remote
United States
Artificial Intelligence • Information Technology • Machine Learning • Marketing Tech • Software • Biotech • Design
Develop and maintain ASC 606 revenue accounting policies, review complex contracts, prepare technical accounting memos, and advise cross-functional teams on revenue implications. Support accurate period-end close activities, audits, and revenue reconciliations. Build scalable templates, playbooks, and documentation while identifying automation opportunities. The role requires strong contract interpretation, technical writing, stakeholder influence, and expertise in technology, subscription, or recurring-revenue environments.
5 Days AgoSaved
Remote
United States
Artificial Intelligence • Information Technology • Machine Learning • Marketing Tech • Software • Biotech • Design
Own the roadmap for Oura’s Business Technology Services and Workato integration platform. Prioritize cross-functional demand, define API contracts and integration patterns, guide build-versus-buy decisions, and connect platform strategy to Databricks data infrastructure and AI agents. Partner with senior stakeholders, Enterprise Architecture, and engineering leadership while establishing intake processes and helping build a permanent Integration and Automation Engineering team.
5 Days AgoSaved
Remote
United States
Artificial Intelligence • Information Technology • Machine Learning • Marketing Tech • Software • Biotech • Design
Lead design and evolution of MLOps platform capabilities, workflows, and governance to enable reliable training, deployment, and operation of ML systems. Collaborate across data science and platform teams to standardize tooling (e.g., MLflow), improve reproducibility, scalability, CI/CD, and infrastructure-as-code, and onboard/enable multiple DS teams for production ML at scale.