Labelbox is the leading data-centric AI platform for building intelligent applications. Teams looking to capitalize on the latest advances in generative AI and LLMs use the Labelbox platform to inject these systems with the right degree of human supervision and automation. Whether they are building AI products by using LLMs that require human fine-tuning, or applying AI to reduce the time associated with manually-intensive tasks like data labeling or finding business insights, Labelbox enables teams to do so effectively and quickly.
Current Labelbox customers are transforming industries within insurance, retail, manufacturing/robotics, healthcare, and beyond. Our platform is used by Fortune 500 enterprises including Walmart, Procter & Gamble, Genentech, and Adobe, as well as hundreds of leading AI teams. We are backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures (Google's AI-focused fund), Databricks Ventures, Snowpoint Ventures and Kleiner Perkins.
About the Role
Become a vital part of revolutionizing healthcare through AI! As an AI Tutor, Medicine, you will be instrumental in teaching, refining, and challenging AI models designed to comprehend and navigate the multifaceted world of medicine. Your expertise will ensure these models develop a nuanced and reliable understanding of critical medical domains, including diagnosis and treatment of diseases, patient care, medical imaging analysis, drug discovery and development, biomedical research, and public health. Your role is pivotal in shaping AI that can improve human health on a global scale.
Your Day to Day
- Educate AI: Analyze and refine AI-generated outputs related to a variety of medical specialties. Provide comprehensive feedback and corrections to enhance the accuracy, safety, and efficacy of AI models in understanding complex medical concepts, procedures, and patient cases.
- Problem Solving: Guide AI models through intricate medical problems, presenting clear, step-by-step solutions and explanations. Your expertise will be crucial in training AI to approach medical scenarios with the same rigor and analytical thinking as a trained physician.
- Red Teaming: Utilize your medical knowledge to rigorously test the limits of AI systems in medical applications. Identify potential biases, ethical concerns, safety risks, and unintended consequences to ensure the responsible and ethical development of AI in healthcare.
About You
- MD or a Bachelor's degree in Medicine, Biology, Biomedical Engineering, or a related field.
- Solid foundation in medical sciences, encompassing anatomy, physiology, pathology, pharmacology, and clinical practices.
- Exceptional analytical and critical thinking skills, with the ability to deconstruct and explain complex medical information effectively.
- Strong communication skills, capable of conveying intricate medical concepts in a clear and concise manner to both technical and non-technical audiences.
Excel in a remote-friendly hybrid model.
We are dedicated to achieving excellence and recognize the importance of bringing our talented team together. While we continue to embrace remote work, we have transitioned to a hybrid model with a focus on nurturing collaboration and connection within our dedicated tech hubs in the San Francisco Bay Area, New York City Metro Area, and Wrocław, Poland. We encourage asynchronous communication, autonomy, and ownership of tasks, with the added convenience of hub-based gatherings.
Your Personal Data Privacy: Any personal information you provide Labelbox as a part of your application will be processed in accordance with Labelbox’s Job Applicant Privacy notice.
Any emails from Labelbox team members will originate from a @labelbox.com email address. If you encounter anything that raises suspicions during your interactions, we encourage you to exercise caution and suspend or discontinue communications. If you are uncertain about the legitimacy of any communication you have received, please do not hesitate to reach out to us at [email protected] for clarification and verification.