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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.
The Health Intelligence team is at the forefront of integrating modern AI and LLMs into the Oura experience, transforming how members interact with and learn from their data. We are building the next generation of AI\-powered health guidance at Oura, blending traditional ML with modern LLMs, reasoning systems, and robust evaluation \- not as “chatbots with vibes,” but as rigorously evaluated components that explain decisions, surface trade\-offs, and adapt member journeys over months and years.
As a Senior AI Engineer, you will design, build, and operate the systems that make this possible: LLM\-backed workflows, retrieval and knowledge representations, evaluation pipelines, and personalization logic that together power personalized guidance, proactive engagement, and new AI\-driven product experiences.
You’ll:
- Work with rich longitudinal signals from wearables plus real\-world context.
- Help turn ambiguous health questions into structured, testable AI systems.
- Balance scientific depth, engineering pragmatism, and product impact so millions of members get guidance that respects both their biology and their real lives.
This role is ideal for someone who wants to work end\-to\-end \- from problem framing and model/tooling choices to productionization, evaluation, and iteration \- in a domain where outcomes and behavior change, not clicks, are the primary success metrics.
What you will do
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You don’t need to do all of these on day one, but these are the kinds of problems you’ll own:
- Design and build LLM‑backed product capabilities: Ship user\-facing features that use LLMs and other AI models to deliver personalized insights, guidance, and proactive notifications. Implement safe tool\-calling, retrieval, and orchestration so that AI components behave deterministically where they must and adaptively where they can.
- Own evaluation, quality, and safety for AI workflows: Lead the design and implementation of evaluation frameworks and tooling to measure quality, safety, latency, and cost before and after release. Define the metrics and slices that matter for user\-facing guidance, and integrate evals into the production pipeline.
- Integrate LLMs with personalization and understanding layers: Ground AI behavior in structured user context rather than one\-off prompts. Connect AI components to navigation flows and action systems so guidance turns into coherent, multi\-step programs and one\-tap actions, not isolated tips.
- Contribute to a multi\-LLM and reasoning platform: Prototype and productionize workflows across multiple model providers and configurations, including routing logic and shadow\-mode experimentation. Collaborate with infrastructure and science teams on reasoning, planning, and multimodal use cases.
- Build robust, observable, and cost\-aware systems: Design and implement services and workflows that meet reliability and performance expectations. Take ownership of operational health: debugging production issues, reducing technical debt, and iterating on architecture as the AI surface area and traffic grow.
- Partner cross\-functionally: Work closely with product, data science, research, design, and content to shape problem definitions, constraints, and evaluation plans. Communicate trade\-offs clearly and help the team make principled decisions in a fast\-moving domain.
Requirements
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We’d love to hear from you if you have:
- 2\+ years of hands\-on experience in AI engineering, with a multi\-year background in backend engineering, applied ML, or related roles building production systems.
- Strong proficiency in at least one modern backend or ML language (e.g., Python) and comfort working with cloud\-native services to ship and maintain production features.
- Demonstrated ability to own systems end\-to\-end: from problem framing and data pipelines through modeling and prompting, all the way to deployment, monitoring, and iteration.
- A track record of working in product\-facing teams, shipping to real users rather than only research prototypes, and caring about impact and iteration speed.
- Comfort operating in a fast\-changing AI/LLM domain with ambiguity, balancing rigor with pragmatism and keeping member value and safety at the center.
- Excellent communication and collaboration skills, including the ability to explain complex technical trade\-offs to non\-technical stakeholders and work effectively in cross\-functional teams across time zones.
Nice to haves:
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- Experience with LLM evaluation and tooling: LLM\-as\-judge, rubric\-based scoring, red\-teaming, prompt versioning, or evaluation platforms (internal or external).
- Familiarity with RAG, knowledge graphs, or semantic retrieval systems (e.g., vector search, hybrid retrieval, ontologies, semantic layers) and how they integrate with LLMs.
- Background in personalization, recommendation, or ranking systems, including multi\-objective optimization and guardrails for safety and fairness.
- Exposure to digital health, wearables, behavior change, or related domains, and interest in working on long\-term outcomes and habit formation rather than short\-term engagement spikes.
- Experience with developer tooling, experimentation frameworks, or analytics/observability products, especially internal tools used by multiple teams.
- Prior work in distributed teams across countries and time zones, and comfort working asynchronously when needed.
- Experience mentoring other engineers or scientists, or informally shaping best practices around AI/ML and evaluation in your team.
Benefits
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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), Missouri (MO), Nebraska (NE), South Dakota (SD), Vermont (VT), 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 $147K-$203K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $147K to $203K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
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: $203K - $203K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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