Fallyx is a company that is building the future of fall detection, prevention, and prediction in retirement homes. By leveraging wearable sensors, machine learning, and various other technologies we actively monitor the user's fall risk 24/7. In this position, you will develop extremely robust machine learning models, focusing on binary and multiclass classification to classify falls and activities of daily living(ADLs). This role is not just about technical expertise; it's about contributing to a team that's making a difference in the quality of life for our users.
Responsibilities
• Algorithm Development: Develop and implement machine learning algorithms, specializing in binary and multiclass classification, to improve our fall detection systems.
• Data Analysis: Analyze large datasets of fall data directly from our pilots with senior homes, extract valuable insights, and identify opportunities for improvement.
• Model Deployment: Deploying machine learning models into production-level environments, ensuring scalability and reliability.
• Hardware Integration: Integrate machine learning models with our hardware infrastructure to make our solution more accurate and reliable.
Qualifications
• Strong proficiency in Python and experience with machine learning libraries and frameworks.
• Experience working with time-series data and building production-level software for at least 3 years.
• Formal education is not mandatory but may be indicative of experience. There should also be a general understanding of the pace at which startups strive to move.
• Familiarity with healthcare data standards and regulations. Additionally, there should be a deep understanding of the problem that we are trying to solve.
We want to create the next generation of senior technology that can feasibly be implemented and improve the quality of life for the elderly. We're looking for someone who wants to commit to something and join us on our journey to improve senior tech. This is a full-time job with both equity and salary-based compensation.
Website: https://fallyx.com
