AI Training for Neuroscience (Freelance, Remote)
is a community of subject matter experts from several disciplines who align AI models by creating high–quality data in their field of expertise to build the future of Generative AI. Alignerr is operated by Labelbox. 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
Contribute to the cutting edge of AI development by shaping how AI understands the complexities of the human brain. As an AI Tutor – Neuroscience, you will leverage your expertise to teach, evaluate, and challenge AI models, ensuring they develop a robust and accurate understanding of this critical field. You'll work on teaching AI models about key areas such as: neural pathways and circuits, neurotransmitters and their functions, mechanisms of learning and memory, sensory and motor systems, and the biological basis of neurological disorders.
Your Day to Day
- Educate AI: Analyze and refine AI–generated outputs related to neuroscience concepts, providing detailed feedback and corrections to improve their accuracy and comprehensiveness.
- Shape Understanding: Craft clear and concise explanations for complex neuroscience topics, tailored to be understandable by both AI models and those building them.
- Evaluate Performance: Design and implement evaluation strategies to assess the AI model's comprehension of neuroscience principles.
- Red Team: Proactively identify vulnerabilities, biases, or limitations in the AI's understanding of neuroscience, proposing solutions for improvement.
About You
- Master's degree or Ph.D. in Neuroscience, or a related field, with a strong theoretical and practical understanding of the brain. Alternatively, an MD with a strong background and specialization in neuroscience is acceptable.
- Excellent communication skills, with the ability to break down complex scientific concepts into clear and concise explanations for both technical and non–technical audiences.
- Strong analytical and critical thinking skills, with a passion for exploring the intersection of AI and neuroscience.
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 Wroc??aw, 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 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 for clarification and verification.
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