Senior Algorithm Engineer

VivoSense, Inc.

5d•$120,000 - $160,000•Remote

About The Position

We are looking for an ambitious individual with a passion for learning new things, and a self-starter attitude. As a small company, this Senior Algorithm Engineer will have the opportunity to come in on the ground floor, build greenfield products, enhance existing algorithms, design, and architect the solutions, and share in the success of our rapidly growing company. This person should be comfortable with navigating through an ambiguous environment, coming up with solutions that fit the user’s needs, and then implementing them.

Requirements

Master’s or Ph.D. in a field related to data science, machine learning, computer science, bioinformatics, biomedical engineering,
Understanding physiological data and its clinical or research applications
Proficiency in signal processing techniques of filtering, feature extraction, and time-series analysis
Strong grasp of Programming, with fluency in Python / data manipulation libraries.
Experience in developing and optimizing algorithms for data analysis and machine learning, particularly for physiological data applications. This includes anomaly detection, classification, prediction, and feature engineering.
3 to 7 years of experience

Nice To Haves

Work within a regulated Quality Management System, such as ISO 13485 or 21CFR820
Familiarity with R
C# or C ++ fluency
Able to work in a small team fast paced environment
Experience / Knowledge with any of the following: AWS
Medical terminology, healthcare regulations, and relevant physiological processes.

Responsibilities

Develop and implement data algorithms and machine learning models for tasks such as signal processing, anomaly detection, classification, and prediction using biometric (physiological, anthropometric etc.) data.
Utilize advanced signal processing and mathematical modeling principles to create and enhance algorithms tailored for applications in areas such as activity tracking and sleep analysis.
Clean and preprocess raw physiological data to remove noise, outliers, and errors. Apply filtering, normalization, and feature extraction techniques to prepare the data for analysis.
Evaluate the performance of different algorithms and models using appropriate metrics, such as accuracy, sensitivity, specificity, and AUC-ROC. Choose the best-performing models for specific tasks.
Create informative data visualizations and dashboards to present biometric data and analysis results to healthcare professionals, researchers, and stakeholders.