About the job
NVIDIA is at the forefront of the AI revolution, and our research is shaping the future of large language models. We are looking for a Senior Scientist to join our team and help advance our capabilities in generating synthetic data and privacy-preserving AI. You will contribute to open-source libraries within the NVIDIA NeMo ecosystem that enable high-quality synthetic data generation and data privacy at scale, including context-aware anonymization. This role combines hands-on software engineering with applied research in LLMs and privacy-enhancing methods, and you will collaborate with research, engineering, product teams, and external labs.
Responsibilities
Build LLM-based methods for synthetic data generation, privacy, and context-aware anonymization, with automated evaluation across multilingual text, documents, and multimodal content.
Optimize task-specific LLMs for low-latency, high-throughput inference (distillation, quantization), and scale our frameworks to run in real time.
Design and maintain open-source libraries and SDKs with clean APIs and strong documentation.
Drive software excellence with modern tooling, architecture based on configuration, and professional Git/CI-CD.
Publish original research at top machine learning and AI conferences to maintain NVIDIA's technical leadership.
Mentor interns and junior researchers to develop technical growth within the team.
Qualifications
Minimum
PhD in Computer Science, Machine Learning, Statistics, or a related field, or equivalent experience.
A research background of 2+ years in applied LLM/NLP research and engineering, synthetic data generation, anonymization and PII detection, or related areas. Comparable experience is also considered.
Proven track record of developing or maintaining software libraries used by a broad developer community.
Strong publication record at premier venues such as NeurIPS, ICML, ICLR, ACL or similar.
Preferred
Active contributions to open-source projects, particularly in ML, security, or privacy domains.
Deep technical understanding of LLMs and inference optimization (quantization, distillation, latency/throughput tuning), with frameworks such as vLLM or TGI.
Ability to build and optimize scalable data processing pipelines for large-scale models.
Functional knowledge of global privacy regulations such as GDPR or CCPA.