Alignerr
AI Trainer for Computational Physics (Freelance, Remote)
Job Description
Alignerr.com 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
Shape the future of AI in Computational Physics!
As an AI Tutor, Computational Physics, you will play a direct role in educating and refining cutting-edge AI models using RLHF (Reinforcement Learning from Human Feedback) techniques. Your deep understanding of computational physics will be essential in guiding these models to develop a comprehensive and accurate grasp of critical subject areas. This position allows for flexible scheduling, and your contributions will directly impact the advancement of AI in Physics.
Your Day to Day
- Teaching: Craft clear, insightful explanations and examples to guide AI models toward a deep understanding of key computational physics concepts.
- Evaluation: Rigorously assess the performance of AI models by designing and conducting tests that challenge their capabilities in solving computational physics problems.
- Red Teaming: Identify potential biases, limitations, or inaccuracies in the AI models' understanding, proactively addressing them to ensure robust and reliable performance.
- Collaboration: Work closely with our team of AI researchers and engineers, providing your subject matter expertise to improve training methodologies and refine the AI models' capabilities.
About You
- Master's degree or Ph.D. in Computational Physics, or a related field, with a strong foundation in theoretical and practical aspects of the discipline. Exceptional candidates with a Bachelor's degree in Computational Physics.
- Excellent communication skills, with the ability to convey complex scientific information clearly and concisely, both verbally and in writing.
- Strong analytical and problem-solving skills, with a meticulous attention to detail and a passion for scientific accuracy.
- Interest in the application of AI in scientific research and a drive to contribute to the development of cutting-edge technologies.
Alignerr strives to ensure pay parity across the organization and discuss compensation transparently. The expected hourly rate range for United States-based candidates is below. Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location.
Important Information
This is a freelance position compensated on an hourly basis. Please note that this is not an internship opportunity. Candidates must be authorized to work in their country of residence, and we do not offer sponsorship for this 1099 contract role. International students on a valid visa may be eligible to apply; however, specific circumstances should be discussed with a tax or immigration advisor. We are unable to provide employment documentation at this time. Compensation rates may vary for non-US locations.
Alignerr
This company profile is still being completed. More information will be available shortly.
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