ML Engineer — LLM Privacy

Capitolis; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Dynamo AI is seeking a highly capable ML privacy specialist to own privacy-focused initiatives, from detection of data leakage to PII protection, and to apply algorithms in customer-facing applications with engineering collaboration.

You will generate synthetic data, train LLMs, and rigorously evaluate and benchmark models, contributing to responsible AI deployment and privacy-preserving technology leadership.

Qualifications

• Deep domain knowledge in privacy-preserving ML.
• Practical experience in techniques to attack or defend ML models in terms of privacy.
• Extensive experience in implementing multiple different types of LLM models and architectures in the real world.
• Adaptability and flexibility in switching focus with new findings in the community.
• Preferred: previous projects or research in LLM privacy.

Responsibilities

• Own an ML privacy vertical e.g. data leakage attacks, sensitive PII detection, and/or membership inference attacks.
• Collaborate with our engineering team to deliver real-world applications of your algorithms for our customers.
• Generate high quality synthetic training data, train LLMs, and conduct rigorous evaluation and benchmarking.

Skills

Privacy-preserving ML

ML privacy attacks/defense

LLM architectures

Adaptability

LLM privacy background

Job description

At Dynamo AI, we believe that LLMs must be developed with safety, privacy, and real-world responsibility in mind. Our ML team comes from a culture of academic research driven to democratize AI advancements responsibly. By operating at the intersection of ML 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 Ph.D.’s and builders, free of Big Tech / academic bureaucracy and constraints.
• Are excited at the idea of democratizing state-of-the-art research on safe and responsible AI.
• 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.
• Care about building a platform to empower fair, unbiased, and responsible development of LLMs and don’t accept the status quo of sacrificing user privacy for the sake of ML advancement.

Responsibilities

• Own an ML privacy vertical e.g. data leakage attacks, sensitive PII detection, and/or membership inference attacks.
• Collaborate with our engineering team to deliver real-world applications of your algorithms for our customers.
• Generate high quality synthetic training data, train LLMs, and conduct rigorous evaluation and benchmarking.

Qualifications

• Deep domain knowledge in privacy-preserving ML.
• Practical experience in techniques to attack or defend ML models in terms of privacy.
• Extensive experience in implementing multiple different types of LLM models and architectures in the real world. Comfortability with leading end-to-end projects.
• Adaptability and flexibility. In both the academic and startup world, a new finding in the community may necessitate an abrupt shift in focus. You must be able to learn, implement, and extend state-of-the-art research.
• Preferred: previous projects or research in LLM privacy.

Dynamo AI is committed to maintaining compliance with all applicable local and state laws regarding job listings and salary transparency. This includes adhering to specific regulations that mandate the disclosure of salary ranges in job postings or upon request during the hiring process. We strive to ensure our practices promote fairness, equity, and transparency for all candidates.

Salary for this position may vary based on several factors, including the candidate’s experience, expertise, and the geographic location of the role. Compensation is determined to ensure competitiveness and equity, reflecting the cost of living in different regions and the specific skills and qualifications of the candidate.