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Salary: USD 151,300 - 178,000 / annual
Oura is seeking an Algorithm Scientist to join the Future Physiology team, focusing on implementing and evaluating novel capabilities in multimodal biosensing. This role combines signals from multiple biosensing pathways to support richer physiological insights. You will work hands-on with experimental sensing systems, analyzing data quality, testing prototype algorithms, and helping build core signal pipelines for the next generation of health sensing.
Key responsibilities include:
- Developing and evaluating algorithms: Implement, refine, and benchmark signal processing and machine learning algorithms for multimodal biosignals (PPG, motion, temperature, and related physiological modalities).
- Multimodal data integration: Apply practical methods for signal cleaning, noise reduction, feature extraction, and time-series data integration across novel modalities.
- Cross-functional collaboration: Work with hardware, firmware, and software engineering teams to analyze sensor performance and optimize data collection pipelines.
- Hands-on prototyping: Analyze data from prototype sensors, evaluation kits, and benchtop setups to assess algorithmic feasibility and signal integrity.
- Physiological exploration: Analyze time-series datasets to discover relationships between physical signal features and underlying physiological states.
- Technical documentation: Document findings, present experimental results, and communicate data-driven insights to project leads and cross-functional teammates.
This is a hybrid role based out of the San Francisco office.
REQUIREMENTS:
- 2–4+ years of practical experience (including advanced degree research) in biosignal processing, time-series analysis, or sensor fusion.
- Strong expertise in Python and scientific computing libraries (NumPy, SciPy, pandas, scikit-learn, PyTorch/TensorFlow).
- Solid foundational understanding of human physiological systems and biosensing techniques.
- Practical experience working with noisy, real-world time-series data and designing robust preprocessing pipelines.
- Master's degree or PhD in Biomedical Engineering, Signal Processing, Machine Learning, Electrical Engineering, or a related quantitative field.
- Strong analytical skills with the ability to clearly explain complex data, methodologies, and technical results to cross-functional peers.
NICE TO HAVE:
- Direct experience with consumer health products, wearable sensors, or digital health applications.
- Demonstrated ability to work at a fast, experimental pace—rapidly prototyping, testing, iterating, and adapting based on empirical feedback.
- Self-starter attitude with proactive approach to identifying data anomalies, unblocking technical hurdles, and initiating data-driven research directions.
