Research Scientist - Privacy

At DynamoFL, we believe that LLMs must be developed with privacy, personalization, and real-world constraints in mind. Our ML and Privacy teams come from a culture of academic research driven to democratize AI advancements responsibly. By operating at the intersection of privacy research and industry applications, our team empowers Fortune 500 companies’ adoption of frontier research for their next generation of LLM products. Join us if you:

• Wish to work on the premier platform for private and personalized LLMs. We provide the fastest end to end solution to deploy research in the real world with our fast-paced team of ML/Privacy Ph.D.’s and builders, free of Big Tech / academic bureaucracy and constraints.
• Care about building a platform to empower private, fair, unbiased, and responsible development of LLMs, and don’t accept the status quo of sacrificing user privacy for the sake of ML advancement.
• Are excited at the idea of democratizing state-of-the-art research (performance, compression, and privacy optimizations) for all companies and products
• Are motivated to work at a 2023 CB Insights Top 100 AI Startup and see your impact on end customers in the timeframe of weeks not years.

Responsibilities

• Research and Development: Conduct research to stay up-to-date with the latest advancements in the field of differential privacy. Leverage this knowledge to design and develop innovative privacy-preserving solutions tailored to the specific needs of our products and services.
• LLM Attack Analysis: Conduct in-depth analysis of LLM attacks to understand potential privacy vulnerabilities in our data processes and systems. Identify areas where linkage attacks may pose a risk to individual data privacy.
• Privacy Impact Assessment: Conduct privacy impact assessments to evaluate potential privacy risks associated with various data-related initiatives and provide recommendations for risk mitigation.
• Differential Privacy Enhancement: Utilize your expertise in differential privacy to design and implement mechanisms that effectively counter LLM attacks. Develop novel algorithms and protocols to protect user data while preserving data utility for accurate analytics.

Qualifications

• Familiarity with privacy frameworks and tools, such as PyDP, TensorFlow Privacy, and OpenDP.
• Proficiency in programming languages commonly used for privacy preservation, such as Python, PyTorch, or TensorFlow.
• Excellent analytical and problem-solving skills, with the ability to communicate complex technical concepts effectively.
• Strong understanding of data privacy regulations and industry best practices.
• Advanced degree (Master's or Ph.D.) in Computer Science, Data Science, Mathematics, or a related field with a focus on privacy and security.
• Prior experience in analyzing and mitigating linkage attacks and privacy risks.