Clinical Data Labeler

Join Our Mission at Sensi.AI Sensi.AI is transforming the world of home care through agentic AI - and we’re just getting started. As a hyper-growth startup, every team member has the opportunity to make a real impact on people’s lives while working as part of a global, collaborative team in a flexible hybrid environment. About The Role The Clinical Data Labeler contributes significantly to the training of our AI models, which aim to enhance the quality of life for senior citizens. This position involves meticulously tagging and classifying textual data based on clinical and technical criteria to ensure high-quality AI training datasets. Key Responsibilities: • Review and analyze clinical textual data, including speech and other relevant sounds. • Accurately tag and classify textual data using clinical and technical criteria. • Collaborate with the AI team to refine and improve labeling guidelines. • Communicate regularly with the team to report challenges, suggest improvements, and ensure consistent data labeling. • Maintain a high level of attention to detail to ensure quality and accuracy in data labeling.

Requirements: • Clinical knowledge and experience or relevant recent studies (e.g. nurse, paramedic, medical student, etc.) • Language Proficiency: You are fluent in English at a native or near-native level, with strong written and verbal communication skills. Spanish proficiency is an advantage. • Goal-Oriented & Detail-Focused: You are highly motivated, able to meet deadlines, and work efficiently while maintaining accuracy and attention to detail. • Digital Literacy: You are comfortable working with digital tools and AI-driven platforms, adapting quickly to new technologies and workflows. • Team Player & Collaborative Mindset: You work well in a team-oriented environment, contributing to shared goals while maintaining clear and open communication. • Customer-Centric Approach: You are dedicated to customer success, ensuring high-quality outputs that align with the company’s mission and values.