Note: The job is a remote job and is open to candidates in USA. NVIDIA is at the forefront of the AI revolution, and they are seeking a Senior Scientist to join their team and help advance capabilities in synthetic data generation for training frontier models. The role involves building data generation pipelines, advancing multimodal data generation, and contributing to open-source libraries within the NVIDIA NeMo ecosystem.
Responsibilities
- Build synthetic data generation pipelines using LLM-based methods and automated quality evaluation, producing datasets that improve the pre- and post-training of LLMs such as Nemotron — reasoning, coding, structured output, and multimodal understanding
- Advance multimodal synthetic data generation — image, document, video, and audio — in partnership with NVIDIA's model teams
- 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
Skills
- PhD in Computer Science, Machine Learning, Statistics, or a related field, or equivalent experience
- A research background of 3+ years in synthetic data generation, generative modeling, multimodal machine learning, or related areas. Comparable experience is also considered
- Deep technical understanding of LLMs, how data shapes their pre- and post-training, and inference frameworks such as vLLM or TGI
- 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
- Open-source contributions in ML or data tooling
- Experience with multimodal generation or understanding (vision-language, document AI, video, or audio)
- Building and optimizing scalable data pipelines for large-scale model training (throughput, distributed inference)
- Experience generating data for agentic, tool-use, or reinforcement-learning post-training
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