Research Fellow

Company Description PrivacyScrubber is a pioneer in Zero-Trust Data Sanitization (ZTDS) for enterprise generative AI workflows, enabling organizations to use public LLMs without compromising data sovereignty or compliance. The platform runs entirely in the browser’s volatile RAM under a Zero-Server architecture, with no backend, API logs, or cloud storage. Its offline-capable design provides strong guarantees against data exfiltration while supporting advanced local processing such as data masking, Named Entity Recognition (NER), and OCR. PrivacyScrubber aligns with GDPR, HIPAA, SOC 2, and ISO 27001 data masking controls, serving diverse teams in HR, legal, healthcare, and DevOps that need secure, local data de-identification.
Role Description The Research Fellow will support PrivacyScrubber’s mission by conducting applied research on privacy-preserving AI workflows, data sanitization methods, and security guarantees for LLM-based tools. Responsibilities include reviewing academic and industry literature, designing and documenting experiments, analyzing outcomes, and summarizing findings in clear, accessible reports and presentations. The Fellow will collaborate with product, engineering, and compliance stakeholders to translate research insights into practical recommendations, prototype evaluation frameworks, and help validate adherence to regulatory standards. This is a remote contract role, with flexible hours and deliverables focused on well-scoped research projects and written outputs.
Qualifications
Strong research skills, including literature review, experimental design, and data analysis in fields such as privacy, security, or AI.
Knowledge of generative AI, LLMs, and related areas (e.g., prompt engineering, data masking, NER, OCR, or secure architectures).
Ability to produce clear technical documentation, research summaries, and structured reports for both technical and non-technical audiences.
Familiarity with regulatory and compliance frameworks (e.g., GDPR, HIPAA, SOC 2, ISO 27001) or a strong interest in learning these domains.
Comfort working independently in a remote environment, managing timelines, and collaborating asynchronously with a distributed team.
Relevant academic background (e.g., computer science, data science, information security, privacy engineering, or related field) or equivalent research experience.
Experience with basic programming, scripting, or data tools (such as Python, R, or similar) for prototyping and analysis is a plus.
Prior work or publications in privacy, cybersecurity, or responsible AI are beneficial but not required.

Work arrangement
Yes