Interested in this AI/ML Engineer role at oura?
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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.
About the Role
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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 a next\-generation AI\-powered platform at the intersection of classical ML and modern GenAI. The serving layer increasingly runs through LLMs, which translates insights from traditional ML into contextually relevant, safe, and personalized insights. Bridging the gap and owning the pipeline of classical ML, backend engineering, and GenAI is one of the defining technical challenges of this role.
As a Staff AI Scientist, you will own the end to end development of critical P1 Health Intelligence initiatives within Oura. You will be hands\-on in building, deploying, and iterating on production systems, and you will hold a high bar for the velocity at which the team moves from hypothesis to live experiment to learning. You will work across the full stack of the product development lifecycle — from ideation, research, data engineering, and pipeline generation to Backend API contracts, LLM configuration, fine tuning, retrieval, and evaluation. You will be part of a bespoke versatile high impact team that is the connective tissue between engineering, product, and design. This is a high\-visibility role for someone who thinks in systems, ships with urgency, and wants to build something that compounds in value over weeks and months.
This is a US Remote role.
What You Will Do
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- Own end to end development of core intelligent capabilities: Research, build, evaluate, and ship reusable intelligence systems—from scientific signal through product integration, launch, and iteration.
- Define improvements in personalization tech strategy: Set the agenda for how Oura represents users and delivers relevant content across surfaces. Influence roadmap and technical direction across partner teams.
- Drive evaluation rigor: Design measurement frameworks that assess the full intelligent Advisor experience. Understanding evaluation only matters if it moves fast enough to inform the next decision — you will build lightweight offline evals and shadow\-mode testing infrastructure that let the team iterate quickly without waiting for long A/B cycles. Establish rubrics and tooling others can use and reuse.
- Support causal and counterfactual model development: Support the causal and counterfactual reasoning necessary to distinguish outcome effects from confounding variables. Design and analyze experiments that measure genuine impact on behavior and health, not just engagement.
- Mentor and raise the bar As a Staff scientist, you are expected to grow the people around you by providing technical mentorship to scientists and engineers — shaping team norms around experimentation and evaluation, and helping define what good looks like for personalization science at Oura.
- Collaborate and communicate across functions Partner with engineering, science, product, and design across the Health Intelligence team to shape how personalization integrates into the broader member experience. Communicate trade\-offs, uncertainty, and modeling assumptions clearly to technical and non\-technical stakeholders across the US and EU.
Requirements
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We’d love to hear from you if you have:
- 8\+ years of experience in applied AI, AI research, and backend engineering. A graduate degree (MS or PhD) in a relevant quantitative field such as Computer Science, Statistics, or a related discipline is strongly preferred.
- Deep experience with AI / LLM\-backed products and evaluation workflows, such as LLM\-as\-judge, rubric\-based evaluation, safety/red\-teaming, and offline vs. online assessment of model quality, latency, and cost. And a track record of shipping these into real production systems in a robust experimentation framework, not just offline analyses or research prototypes.
- Deep experience with Backend engineering best practices and demonstrated ability to build and own systems that serve millions of users.
- Hands\-on experience across retrieval, ranking, and recommendation system design (including collaborative filtering, embedding\-based approaches, graph networks, or related methods), and a track record of shipping these into real production systems in a robust experimentation framework, not just offline analyses or research prototypes.
- Comfort working closely with server and app engineers on model serving, pipeline architecture, and deployment infrastructure — and an instinct for where to cut scope to ship faster.
- Practical experience integrating recommendation or retrieval signals with LLM\-powered generation, including work on grounding, constrained decoding, prompt design, or evaluation frameworks that assess the efficacy of the generation layer.
- Demonstrated ability to design lightweight experiments and evaluations that generate signal quickly, such as shadow testing, staged rollouts, and proxy metrics that responsibly accelerate the learning loop without waiting on long A/B cycles.
- Experience framing personalization problems, modeling user trajectories, and working with stateful or sequential data.
- Solid exposure to causal methods (uplift modeling, treatment effect estimation, counterfactual evaluation) and experiment design, with the ability to interpret results with appropriate caution and communicate uncertainty clearly.
- Evidence of operating beyond individual contributions: influencing technical direction, mentoring others, shaping team practices, or leading cross\-functional scientific initiatives.
- Strongability to explain complex systems, trade\-offs, and uncertainty to both technical and non\-technical audiences, and to operate effectively in a fast\-moving, ambiguous domain.
- Strong proficiency in Python, including data analysis and modeling, as well as experience with modern data tooling in collaboration with data and engineering partners.
Would be a benefit
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These are strong signals of fit:
- Experience designing personalization specifically for consumer behavior change or health outcomes, where the goal is engagement and longitudinal impact.
- Familiarity with health, wearables, or digital therapeutics domains, and genuine interest in how personalization compounds over a member's lifetime.
- Comfort working outside “core” hours across time zones with distributed, cross\-functional teams.
What we offer
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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: $233,000 \- $267,950
- Region 2: $212,000 \- $243,800
- Region 3: $199,000 \- $228,850
A recruiter can determine your zones/tiers based on your U.S. 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), 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 $233K-$267K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($250K) sits 17% above the category median. Disclosed range: $233K to $267K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
oura AI Hiring
oura has 2 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Based in Remote, US. Compensation range: $203K - $267K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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