Senior Director - GenAI Data Strategy

NVIDIA•Santa Clara, CA

About The Position

As the Senior Director -Gen AI Data Strategy, you will own the comprehensive data strategy that fuels our most sophisticated foundation models. Data is the architecture of intelligence, moving beyond simple data fulfillment to become a visionary architect of our "data flywheel". Partner with top research scientists and engineers to identify model failure modes and bridge performance gaps through strategic data acquisition, curation, and synthetic generation.

Requirements

Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or a related technical field (or equivalent experience).
18+ overall years of experience in product management or data operations specifically within the AI/ML sector at a technology company.
8+ years direct people management experience
Frontier Model Knowledge: Deep understanding of LLM/VLM architectures, training regimes, and alignment methods like RLHF and RLAIF.
Data-Centric Attitude: A "Full Stack" data perspective, with a proven track record of managing large-scale data pipelines and dealing with diverse modalities.
Customer Empathy: Experience working directly with enterprise customers or SAs to identify product capability gaps and translating those into concrete data collection requirements.

Nice To Haves

Open Data & Ecosystem Strategy: Experience releasing public datasets or driving data-as-a-resource initiatives that activate an external developer community.
Industry Standardization: Experience driving industry-wide standards for data formats and quality metrics.
Tooling Proficiency: Exposure to data management and labeling platforms (e.g., Scale.ai, Labelbox) and technical tools like Python, SQL, or Spark.

Responsibilities

Holistic Data Strategy & Roadmap: Define and evolve the end-to-end roadmap for multi-lingual data acquisition across various modalities (text, vision, audio, etc.) to train and evaluate large-scale AI models. Data sources include:Pre-training: Large scale knowledge-based data collection. Post-training: Planning and collecting for next generation agentic capabilities. Building a golden bench set for both academic and real world measurements.
The Data Flywheel & Failure Analysis: Collaborate with research teams to interpret model edge cases and failure modes. Use these insights to refine data collection logic, crafting a continuous loop where model output informs the next phase of data acquisition.
Strategic Data Acquisition: Manage an external ecosystem of data partners and vendors. Establish meticulous qualification and acceptance criteria, ensuring all licensed assets meet legal, ethical, and technical standards.
Human-in-the-Loop (HITL) & Alignment: Lead the strategy for high-quality human data collection, including RLHF (Reinforcement Learning from Human Feedback), SFT (Supervised Fine-Tuning), and complex "human-in-the-loop" workflows to ensure model safety and alignment.
Synthetic Data Innovation: Orchestrate the synthetic data stream used to bootstrap and amplify model fine-tuning and evaluation techniques, effectively bridging the gap between real-world scarcity and infinite scale.
Customer & Ecosystem Engagement: Partner with Solutions Architects and enterprise customers to translate real-world deployment gaps into data priorities. Contribute to NVIDIA's open data strategy, defining benchmarks for developer adoption and dataset release.
Quality & Governance Frameworks: Establish data quality frameworks, including de-duplication, versioning, bias detection, and ethical filtering. Lead internal policies on data privacy, consent, and transparency.