A Machine Learning Engineer at Oura sits at the intersection of cutting-edge hardware, biomedical science, and large-scale data processing. You will build and scale the intelligent algorithms that power the Oura Ring, a revolutionary wearable device that tracks sleep, activity, readiness, and overall physiological health. Your primary objective is to transform noisy, continuous streams of raw sensor data into highly accurate, personalized, and actionable health insights for millions of users worldwide.

Unlike traditional software engineering roles, your work will directly influence user well-being and clinical-grade health tracking. You will tackle complex problems such as sleep stage classification, stress detection, cardiovascular health assessment, and activity recognition. This requires a deep understanding of time-series analysis, signal processing, and how to deploy robust machine learning models that can run efficiently on either the companion mobile application or cloud infrastructure.

As a Machine Learning Engineer, you will collaborate closely with cross-functional teams, including hardware engineers, firmware developers, product managers, and health researchers. You will play a vital role in translating scientific discoveries into production-ready software features. If you are passionate about digital health and want to build models that have a tangible impact on human lives, this role offers an exceptionally rewarding and technically stimulating environment.

To help you prepare, we have categorized representative questions based on real interview experiences for the Machine Learning Engineer role at Oura. These questions reflect the typical focus areas of the hiring team, emphasizing practical application over rote memorization.

Data Engineering & Signal Processing

Because the Oura Ring relies heavily on raw sensors, you must demonstrate a strong ability to clean, process, and extract features from noisy time-series data.

• Given a raw accelerometer dataset, how would you approach noise reduction and signal filtering?
• Describe your strategy for handling missing or corrupted sensor data streams over a multi-hour sleep period.

Preparing for an interview at Oura requires a balanced focus on technical mastery, signal processing fundamentals, and collaborative communication. You should approach your preparation with a holistic mindset, ready to demonstrate not just how you build models, but why your technical choices make sense for a health-tech product.

Role-Related Knowledge – You must show a deep understanding of time-series data, signal processing, and fundamental ML algorithms. Be ready to discuss how you handle noisy sensor streams, extract meaningful features, and select appropriate model architectures. Familiarity with physiological data is a significant advantage.

Practical Problem-Solving – Oura interviewers evaluate your ability to approach ambiguous, open-ended problems systematically. You should be able to break down a high-level goal (like "detecting stress levels") into concrete steps: data collection, feature engineering, model selection, validation, and deployment.

Cross-Functional Collaboration – Since ML engineers at Oura work closely with researchers, hardware teams, and product managers, you need to show that you are an empathetic and effective communicator. You must be able to articulate the business and user impact of your engineering decisions.

Product & User Empathy – You should understand the Oura Ring product ecosystem and have a genuine interest in wearable technology and digital health. Think about how machine learning can directly improve the user experience, making health data intuitive, supportive, and actionable.

The interview process at Oura is designed to be thorough, fast, and highly respectful of the candidate's time. Candidates consistently report that the process is exceptionally well-organized, with clear communication from recruiters at every stage. Rather than relying on high-pressure live coding grinds, Oura focuses on practical, discussion-based evaluations that mirror real-world engineering challenges.

The journey typically begins with an initial HR screening call to discuss your background, career goals, and alignment with Oura's mission. This is followed by a technical take-home assessment, which serves as the primary filter for your practical coding and data analysis skills. Once you pass the take-home exam, you will meet with the hiring manager to discuss your experience and the team's technical direction. The final stage consists of a series of structured 1:1 interviews focusing on deep technical discussions, system design, cross-functional collaboration, and behavioral fit.

The visual timeline above outlines the typical progression from your initial application to the final offer decision. You should expect the entire process to take approximately 3 to 4 weeks, depending on scheduling availability. Use this timeline to pace your preparation, ensuring you dedicate ample time to the take-home assignment and the subsequent technical discussions.

Sensor Data & Feature Engineering

This area is the foundation of the technical evaluation at Oura. Because the company's primary data source is raw sensor streams, you must prove that you can manipulate, clean, and extract value from noisy, high-frequency signals.

To perform strongly, you must show that you do not just feed raw data blindly into a neural network. Instead, you should demonstrate a methodical approach to understanding physical signals, isolating noise, and extracting high-quality features that represent human physiology and movement.

Be ready to go over:

• Signal preprocessing – Noise reduction techniques, handling sensor dropouts, calibration, and normalizing time-series data.
• Feature extraction – Creating statistical, frequency-domain, and time-domain features from accelerometer and PPG sensors.
• Data windowing – Selecting appropriate window sizes (e.g., 30-second epochs for sleep, 5-second windows for activity) and handling overlapping segments.
• Advanced concepts (less common) – Fourier transforms, wavelet transforms, and bandpass filtering to isolate specific physiological frequencies.

Example scenarios:

• "You are given a raw accelerometer data stream from a user who is sleeping. How do you identify and filter out movement artifacts that do not represent actual sleep disturbances?"
• "Explain how you would design a feature set to distinguish between a user running versus a user washing dishes, based solely on wrist-based motion data."

Supervised ML Strategy & Modeling

This evaluation area focuses on your ability to select, train, and validate machine learning models. Oura values engineers who can justify their modeling choices based on constraints like model size, latency, interpretability, and data availability.

A strong performance involves demonstrating a rigorous validation strategy. Because physiological data is highly personalized, standard train-test splits can lead to data leakage if data from the same user appears in both sets. You must show awareness of these domain-specific pitfalls.

Be ready to go over:

• Validation strategies – Group K-Fold cross-validation (splitting by user), handling highly imbalanced datasets, and setting up robust offline evaluation frameworks.
• Model selection – Knowing when to use simple, highly interpretable models (e.g., logistic regression, decision trees) versus complex architectures (e.g., gradient boosted trees, LSTMs).
• Performance metrics – Choosing the right metrics (F1-score, precision-recall AUC, sensitivity) depending on the clinical or user-facing impact of model errors.
• Advanced concepts (less common) – Semi-supervised learning for leveraging large volumes of unlabeled wearable data, and transfer learning for personalizing models.

Example scenarios:

• "How would you structure your cross-validation to ensure that a newly trained activity classifier generalizes well to new users who were not part of the training set?"
• "If your model has a high rate of false positives for detecting a specific health event, what steps would you take to tune the model, and how would you measure the impact on user experience?"

Cross-Functional Collaboration & System Design

At Oura, machine learning does not exist in a vacuum. Your models must integrate into a broader product ecosystem and run within the constraints of mobile devices and cloud infrastructure. This area evaluates how you design systems and work with other teams to bring models to life.

You will need to demonstrate that you can think about the entire life cycle of a model, from research and development to deployment, monitoring, and iterative improvement, while maintaining strong alignment with product goals.

Be ready to go over:

• On-device vs. cloud processing – Evaluating latency, battery consumption, privacy, and data transfer costs to decide where a model should run.
• API and contract design – Designing clean interfaces between machine learning services and mobile or backend systems.
• Model monitoring – Setting up alerts for model drift, concept drift, and tracking key performance indicators in production.
• Advanced concepts (less common) – Quantization and compression techniques to fit deep learning models onto resource-constrained mobile hardware.

Example scenarios:

• "Walk me through how you would design a system to compute and deliver a daily 'Readiness Score' to millions of users every morning at 6:00 AM local time."
• "How would you collaborate with a UX designer and a firmware engineer to launch a new real-time workout tracking feature?"