# Senior Scientist, Synthetic Data Generation

**Company**: NVIDIA
**Location**: Santa Clara, CA
**Work arrangement**: remote
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Technology

**Apply**: https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/US-CA-Santa-Clara/Senior-Scientist--Synthetic-Data-Generation_JR2019460?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_e67d5e46-abd

## Description

We are looking for a Senior Scientist to join our team and help advance our capabilities in synthetic data generation for training frontier models. You will contribute to open-source libraries within the NVIDIA NeMo ecosystem that generate synthetic datasets across text, code, structured, and multimodal data, directly feeding the pre- and post-training of LLMs such as Nemotron.

This role combines hands-on software engineering with applied research in generative methods, and you will collaborate with research, engineering, product, and model teams as well as external labs.

**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.

**Requirements:**

- 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.

**Preferred Qualifications:**

- 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.

## Skills

### Required
- PhD in Computer Science, Machine Learning, Statistics, or a related field
- Research background in synthetic data generation, generative modeling, multimodal machine learning, or related areas
- 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

### Nice to have
- 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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