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About This Role
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.
At Ōura, we're on a mission to make health a daily practice for everyone. With our award-winning Ōura Ring and app, we've empowered over a million people to improve their sleep, understand their bodies, and transform their health. Now, we're looking for a distinguished AI leader to turn scattered experimentation into safe, scalable, everyday practice across the entire company.We are seeking a Staff AI Data Transformation Architect to lead as the senior-most individual contributor driving Data AI enablement across our Data team at Ōura, reporting to VP, Data Engineering and Analytics. This is a highly visible, org-defining role that combines two things usually split across separate jobs: the strategic work of building a company-wide AI roadmap and operating model, and the hands-on work of running the operational backbone — access, governance, licensing — that makes that practice safe and scalable.There are no direct reports in this role: strategy, influence, credibility, and hands-on execution are your primary tools. This role is distinguished from Senior by its company-wide scope of influence, the level of ambiguity it resolves, and the expectation that you define the problem as much as solve it.
What You Will Do:
- Define and own the Data AI enablement roadmap across data, product, engineering, science and business functions, prioritizing the transformation opportunities with the highest leverage.
- Build the operating model for repeatable adoption: playbooks, shared tooling, evaluation frameworks, and success metrics that turn isolated pilots into standard practice.
- Establish a shared language and evaluation framework that leaders across the company use to assess AI opportunities consistently.
- Drive build-vs-buy decisions for AI tooling, influencing vendor strategy and technology investment at a company-wide level.
- Own the AI tool estate end-to-end: integration of data and access models within the Databricks platform, multi-agent workflows, token management, and partner with AI Productivity to power the AI ecosystem with a trusted data layer.
- Hold the governance line: partner with AI Governance, Security, Privacy, and Legal to define guardrails for responsible AI use in a health-sensitive environment, with auditable change management.
- Keep AI spend efficient: own observability, productivity best practices and reclamation cycles, and the usage insights that inform renewal, expansion, and build/buy decisions.
- Architect Ōura's Data AI strategy leveraging Databricks and AWS to process terabyte–petabyte scale data with global consistency.
- Lead the design of machine-readable contracts, vector-based data architectures and Retrieval Augmented Generation (RAG) patterns to enable LLM-powered reporting and Agentic AI at production scale, including an enterprise context plane: ontology, semantic layer, knowledge graph, and context layer.
- Partner closely with data analysts and engineers to assess, prioritize, and remediate data readiness (quality, structure, access, documentation) against the AI roadmap's use cases.
- Partner with leadership to drive workflow redesign, working directly with functional leaders to change how their teams operate — not just what tools they have access to.
- Build internal AI fluency through reusable training, a strong practitioner community, and self-service documentation that let teams and other admins operate independently.
- Scale a small number of high-value pilots into standard, company-wide practice rather than leaving them as isolated experiments.
- Act as the primary bridge between executive intent, functional leaders, and enabling functions (Security, Legal, IT, Software).
- Drive alignment across AI productivity, engineering and business units, establishing trust early and earning a seat at the table through credibility.
- Set the operating standard that functional AI Champions and any dedicated AI-tool administrators run to, without owning their day-to-day work directly.
- Represent Data AI enablement in cross-company forums, making adoption and impact metrics visible and discussed at the leadership level.
This is a hybrid role out of our San Francisco office.
What you'll bring:
- 8+ years of experience scaling AI data solutions, products or technology adoption beyond pilots into sustained, organization-wide practice, with measurable outcomes.
- A track record of building durable operating models, SOPs, and measurement systems that outlast any one project.
- History of influencing and aligning senior leadership on long-horizon technology investments.
- Deep technical fluency in LLMs and implementation patterns, with sound judgment for separating real value from hype.
- Direct, hands-on experience administering at least one enterprise AI productivity tool (e.g., Cursor, Claude, ChatGPT Enterprise, Glean, Gemini, coding assistants, LLM API gateways), including user/group management and configuration troubleshooting.
- Demonstrated AI fluency: uses LLM assistants and agents in daily work with sound judgment and verification.
- Clear point of view on when to build vs. buy, grounded in real experience rather than generic opinion.
- Experience in enterprise data platforms including lakehouse using AI/LLMs.
- Practical experience with role-based access models and permissions in multi-domain/region zones.
- Comfort partnering with Security, Privacy, and Legal to define and operationalize guardrails for responsible AI use in a regulated or health-sensitive environment.
- Ability to influence senior stakeholders and drive alignment across R&D and business units without formal authority.
- Proven ability to design, document, and maintain SOPs, playbooks, and training that non-technical users and other admins can follow independently.
- Strong written and verbal communication skills, able to flex between a hands-on troubleshooting conversation and an executive steering committee.
Nice to have:
- Databricks Certified Administrator or Databricks Certified Data Engineer certification.
- Familiarity with data mesh principles and domain-oriented data ownership models.
- Experience supporting ML workflows — MLflow, Feature Store, or Databricks Model Serving.
- Knowledge of dbt for data transformation and analytics engineering workflows.
- Cloud/AI token FinOps experience — cost allocation tagging, budget alerting, and multi-tier storage optimization.
- Exposure to Databricks Lakehouse Federation or cross-platform query federation patterns.
- Prior experience as an AI Champion or similar transformation role in another function or company.
- Familiarity with scripting or configuration automation (e.g., Python, shell, configuration-as-code) to reduce manual admin work.
- Experience in health tech, consumer health, wearables, or another regulated, high-trust industry.
- Deep expertise designing data architectures for LLM, RAG, and vector-based use cases in production environments.
- Experience with MLOps frameworks, VertexAI, MLflow, and production AI/ML lifecycle management.
- Ability to define data requirements for Agentic AI and interactive self-serve analytical systems.
Benefits
At Ōura, we care about you and your well-being. Everyone here at Ōura 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 Ōura 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
Ōura 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 $198,050 - $233,000
Region 2 $180,200 - $212,000
Region 3 $169,150 - $199,000
A recruiter can determine your Region 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.
Salary Context
This $169K-$233K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At oura, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($201K) sits 5% below the category median. Disclosed range: $169K to $233K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
oura AI Hiring
oura has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $233K - $233K.
Location Context
AI roles in San Francisco pay a median of $264,500 across 2,413 tracked positions. That's 24% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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