Nvidia Data Product Manager Generative AI 

United States, California 

399356129

What you’ll be doing:

• Lead the generative data roadmap—define and evolve our strategy for synthetic, curated, and real-world data used to train, fine-tune, and evaluate large-scale AI systems for a specific modality or model type.

• Craft and lead data pipelines that power model customization, alignment, and evaluation—including synthetic data generation, human-in-the-loop (HITL) workflows, and RL loops.

• Establish thorough data quality frameworks, including coverage analysis, bias detection, adversarial testing, and ethical filtering.

• Partner with AI researchers and engineers to identify data bottlenecks in model development, then deliver data, tools or workflows to unblock them.

• Define product requirements for internal tools supporting data collection, annotation, and augmentation at scale.

• Collaborate with enterprise customers to design workflows for domain-specific LLM fine-tuning using synthetic or proprietary datasets.

• Drive initiatives on responsible data use, including fairness, visibility, and privacy, in close alignment with legal, compliance, and AI ethics teams.

• Lead cross-functional efforts with infrastructure, model training, and deployment teams to ensure that data is usable, scalable, and aligned with research goals.

• Forge strategic partnerships with academic labs, data vendors, and open-source communities to expand access to high-quality and diverse datasets.

What We need to see:

• Bachelor’s or Master’s in Computer Science, Data Science, AI/ML, or a related technical field (or equivalent experience).

• 8+ years of experience

• Demonstrated ability in product management, data platform leadership, or ML/AI-focused roles at a technology company

• Strong understanding of LLM architecture, training regimes, and alignment methods (e.g. fine-tuning, RL, retrieval-augmented generation)

• Consistent track record in running large-scale data projects or pipelines for machine learning applications

• Deep familiarity with data-centric AI development, from collection to evaluation

• Outstanding communication skills—ability to translate between research, engineering, and business collaborators

• Proven ability to prioritize optimally and ship sophisticated cross-functional projects

Ways to stand out from the crowd:

• Experience with GenAI data workflows: prompt engineering, synthetic data generation, human feedback systems

• Prior work on LLM evaluation frameworks or benchmarks

• Experience developing data quality, labeling, or annotation tools

• Knowledge of data governance, privacy, or AI ethics standard processes

• Exposure to enterprise ML use cases and multi-modal model development