Principal Research Scientist, Synthetic Data Generation

🔥 18 hours ago

🏄 California, Colorado, +2 more states – Remote

🏄 California – Remote ⛰️ Colorado – Remote 🗽 New York – Remote 🍂 Massachusetts – Remote

💵 $272k - $431.3k / year

⏰ Full Time

🔴 Lead

🧬 Research Scientist

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 1%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Set the technical direction for synthetic data generation across NVIDIA's frontier model efforts • Define and build open-source libraries within the NVIDIA NeMo ecosystem • Generate synthetic datasets across text, code, structured, and multimodal data for LLM pre- and post-training • Build and scale LLM-based data generation pipelines with automated quality evaluation • Develop synthetic trajectories, multi-turn interactions, function calling, executable environments, reward modeling, and verifiable-reward data for agentic and tool-use training • Advance multimodal synthetic data generation for image, document, video, and audio • Advance privacy-preserving and safe synthesis using differential privacy, anonymization, and de-identification • Develop and maintain open-source libraries and SDKs with clean APIs and strong documentation • Drive software excellence through modern tooling, configurable architecture, and professional Git/CI-CD • Publish original research at leading machine learning and AI conferences • Collaborate with research, engineering, product, model teams, and external labs • Mentor scientists and engineers across the team

🎯 Requirements

• PhD in Computer Science, Machine Learning, Statistics, or a related field, or equivalent experience • 15+ years of engineering and research experience in synthetic data generation, generative modeling, multimodal machine learning, or related areas • Deep technical understanding of LLMs, pre-training, post-training, RL stages, and inference frameworks such as vLLM or TGI • Proven track record of developing or maintaining software libraries used by a broad developer community • Experience building and optimizing scalable data pipelines for large-scale model training, including throughput, distributed inference, and cost at cluster scale • Strong publication record at premier venues such as NeurIPS, ICML, ICLR, ACL or similar • Significant open-source contributions in ML or data tooling, with community adoption • Experience with multimodal generation or understanding, including vision-language, document AI, video, or audio • Experience generating data for agentic, tool-use, or reinforcement-learning post-training, including RL environment design • Background in differential privacy, de-identification, or synthetic data for regulated industries such as healthcare, finance, or government • Experience influencing model training decisions at frontier scale, or partnering directly with pre-training and post-training teams

🏖️ Benefits

• Equity • Benefits