Algorithm Scientist, Multimodal Biosignals

Oura

San Francisco (CA)

Hybrid

USD 151,000 - 178,000

Full time

14 days+

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Benefits offered by this job

Health, dental, vision insurance

Own Ōura Ring + employee discounts

Paid time off and holidays

Sick leave and parental leave

Job summary

Oura is seeking an Algorithm Scientist to join our Future Physiology team in San Francisco, focusing on multimodal sensing and signal pipelines for next-gen health sensing. You will develop and evaluate algorithms, test prototypes, and collaborate across hardware, firmware, and software teams to optimize data collection and insights.

The role requires 2–4+ years in biosignal processing or time-series work, strong Python skills, and a PhD/Master’s in a quantitative field.

Qualifications

2–4+ years of practical experience 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.

Responsibilities

Develop and evaluate algorithms: implement, refine, and benchmark signal processing and machine learning algorithms for multimodal biosignals (e.g., PPG, motion, temperature).
Multimodal data integration: apply practical methods for signal cleaning, noise reduction, feature extraction, and time-series data integration across novel modalities.
Cross-functional support: collaborate with hardware, firmware, and software engineering teams to analyze sensor performance and optimize data collection pipelines.
Hands-on prototyping: analyze data collected 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 & reporting: document technical findings, present experimental results, and communicate data-driven insights clearly to project leads and cross-functional teammates.