Summary
The job is for an ML Privacy Expert at a software company specializing in deploying large language models (LLMs) using sensitive data. The role involves leading an ML privacy vertical, collaborating with the engineering team, and contributing to research. Extensive experience in federated learning is not mandatory but expertise in privacy-preserving ML methodologies is required.
Requirements
- Profound expertise in privacy-preserving ML methodologies
- Hands-on experience in training Differentially Private models utilizing frameworks such as Opacus, OpenDP, etc
- Extensive practical knowledge in deploying various Large Language Model (LLM) models and architectures in real-world scenarios. Proficiency in leading end-to-end projects is expected
Responsibilities
- Lead and manage an ML privacy vertical focused on specific domains and optimization strategies (e.g., differential privacy, privacy-enhancing algorithms, anonymization)
- Collaborate closely with the engineering team to implement practical applications of your developed algorithms, catering to our clients' needs
- Contribute significantly to the creation of research papers, patents, and presentations by integrating your vertical's work with other team members' research contributions
Preferred Qualifications
Domain expertise in understanding attacks against ML models, especially in Computer Vision (CV) or Large Language Models (LLMs)
