Algorithm Scientist

We are looking for an Algorithm Scientist to join our Future Physiology team. This role focuses on implementing and evaluating novel capabilities in multimodal sensing, combining 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 the core signal pipelines for the next generation of health sensing at Oura.

• →Develop and evaluate algorithms: Implement, refine, and benchmark signal processing and machine learning algorithms for multimodal biosignals (e.g., 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 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.

This is a hybrid role out of our San Francisco office.

• 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.

• Direct experience working with consumer health products, wearable sensors, or digital health applications.
• Demonstrated ability to work with a fast, experimental pace—rapidly prototyping, testing, iterating, and adapting approaches based on empirical feedback.
• A self-starter attitude with a proactive approach to identifying data anomalies, unblocking technical hurdles, and initiating data-driven research directions.