AI Tutor, Data Science

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 critical part of advancing the capabilities of AI in the vital field of data science! As an AI Tutor, you will leverage your expertise to teach, evaluate, and red-team cutting-edge AI models, ensuring they develop a deep and accurate understanding of core data science principles. Your work will directly impact the development of AI that can analyze complex datasets, identify trends, and generate accurate predictions across a range of applications.

We're particularly focused on developing AI proficiency in the following areas:

• Statistical Modeling & Inference: Teaching models to understand and apply various statistical methods for data analysis, hypothesis testing, and drawing meaningful conclusions.
• Machine Learning Algorithms: Guiding models to master the theory and application of supervised, unsupervised, and reinforcement learning algorithms for tasks like classification, regression, and clustering.
• Data Visualization & Interpretation: Training models to effectively communicate complex data insights through clear and informative visualizations.
• Data Ethics and Responsible AI: Instilling in models an understanding of the ethical implications of data science, including bias detection, fairness, and responsible data handling.

Your Day to Day

• Teaching AI: You'll utilize RLHF techniques to train AI models on complex data science concepts and problem-solving approaches.
• Evaluation & Feedback: You'll rigorously evaluate AI model performance, providing detailed feedback and corrective measures to enhance their accuracy and understanding.
• Red Teaming: You'll design and conduct rigorous tests to identify vulnerabilities, biases, and limitations within the AI models' data science knowledge.

About You

• Master's degree or PhD in Data Science, Statistics, Computer Science, or a related field, OR a Bachelor's degree with 3+ years of relevant industry experience
• Strong understanding of core data science principles, including statistical modeling, machine learning algorithms, and data visualization techniques.
• Excellent communication skills, with the ability to articulate complex technical concepts clearly and concisely.
• A passion for pushing the boundaries of AI and its applications within data science.

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.