> Markdown version of [/jobs/ext/3967590-senior-scientist-synthetic-data-generation](https://www.wearedevelopers.com/jobs/ext/3967590-senior-scientist-synthetic-data-generation). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Scientist, Synthetic Data Generation - **Company:** NVIDIA Corporation - **Location:** United States - **Experience:** Expert - **Salary:** $168,000.0 - $264,500.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Data Files, Machine Learning, Open Source Technology, Software Maintenance, Software Engineering, Large Language Models, Git, Information Technology, Free and Open-Source Software, Data Management, Intelligent Document Processing, Data Pipelines, Data Generation - **Published:** October 27, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-scientist-synthetic-data-generation-ma--fd27680b-9665-4b49-a5d5-ce52dacb26f1 ## About the Role Ecosystems, Data Sets, Software Engineering, Open Source, Structured Data, Data Management, Documentation, Application Programming Interface (API), Machine Tool, Git, * 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. Ways to stand out from the crowd: * 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.