About Vena Vitals
Vena Vitals makes a small sticker that monitors blood pressure continuously. We've shown that it works in the operating room and is as accurate as the best tools doctors have today, at a fraction of the cost. We're a team of health tech scientists with multiple past startups and exits, and we've built products that have scaled to over 2M users.
About the role
Skills: Machine learning, C, C++, D, MATLAB, Python, TensorFlow, Deep Learning, Data Modeling, Data AnalyticsAbout Us
Vena Vitals is on a mission to transform patient care through better cardiovascular monitoring. We make a small wearable device that monitors blood pressure continuously. Unlike other BP monitors that only take a snapshot in time, we record the entire dynamic movie to track changes that can be life-saving. We have strong early clinical results, and we’re now expanding our clinical studies across multiple sites around the country. From the operating room to the home, we plan to become the most accurate and actionable cardiovascular monitoring and management system to help improve the lives of over a billion patients across the world each year.
Job Description
You will develop algorithms that incorporate multiple continuous biomedical signals into relevant insights. You will develop tools to enhance signal and subtract out common noise artifacts, as well as extract insights from continuous biomedical signals (features, events, and trends) both off-line and in real time.
You will be engaged in solving problems that currently do not have well defined solutions and you’ll take the reins to own and steer your deliveries, while also supporting others in a fast-paced, encouraging, and dynamic environment.
Key Qualifications
- 4+ years of experience working with physiological signals (extracting, cleaning, and analysis of datasets). Domain knowledge of cardiovascular system and hemodynamics is a plus.
- Experience working with complex data sets that have limited sample sizes.
- Strong background in exploratory and statistical data analysis. Knowledge of design of experiments is a plus.
- Experience in developing algorithms that handle time-series data, which can include but not limited to training and evaluating machine/deep learning models (e.g. tree-based models, LSTM, recurrent neural networks).
- Excellent fluency in a high-level programming language (Python, C/C++ and/or Matlab)
- Knowledge of common ML frameworks (scikit-learn, pandas and deep learning toolboxes such as TensorFlow, PyTorch, Caffe, etc) is a plus.
- Experience in one or more of the following is a plus: signal processing, multimodal sensing, sensor fusion, time series, time-frequency analysis, and dynamical systems
- Masters/PhD in Biomedical Engineering, Electrical Engineering, Computer Science, Mathematics, Physics, or related fields or equivalent work experience.
Additional Qualifications
- Expected to perform well in a fast-paced environment, to execute on the tasks assigned, to meet the production deadlines and, at the same time, to explore independently new innovative ideas.
- Ability to communicate highly technical results and methodologies along with working cross-functionally with a wide range of people
- Ability to review literature related to projects and integrate relevant information.
- Knowledge of medical instrumentation is a plus
Technology
Hardware meets algorithms
