Role at a glance
- Salary
- $340K – $517.5K/yr
- Location
- Santa Clara, California, United States
- Work arrangement
- On-site
- Employment
- Full-time
- Experience
- 18+ overall years of experience in product management or data operations specifically within the AI/ML sector at a technology company;...
- Education
- Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or a related technical field (or equivalent experience).
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Role Summary
The Senior Director, Gen AI Data Strategy owns the comprehensive data strategy supporting sophisticated foundation models, spanning multilingual and multimodal data acquisition, curation, synthetic generation, and evaluation. The role partners with research teams, engineers, Solutions Architects, enterprise customers, data vendors, and external developers to improve model performance, safety, alignment, and deployment capabilities.
What You'll Do
- Define and evolve the end-to-end roadmap for multilingual data acquisition across text, vision, audio, and other modalities for training...
- Collaborate with research teams to interpret model edge cases and failure modes and use those insights to refine data acquisition.
- Manage external data partners and vendors and establish qualification and acceptance criteria for licensed assets.
- Lead human data collection strategies, including RLHF, SFT, and human-in-the-loop workflows for model safety and alignment.
- Orchestrate synthetic data streams for model fine-tuning and evaluation.
- Establish data quality and governance frameworks covering de-duplication, versioning, bias detection, ethical filtering, privacy,...
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View full postingQualifications
Requires a bachelor’s or master’s degree in Computer Science, AI/ML, Data Science, or a related technical field, or equivalent experience; 18+ years of AI/ML product management or data operations experience at a technology company; 8+ years of direct people management experience; deep knowledge of LLM/VLM architectures, training regimes, and RLHF/RLAIF; experience managing large-scale, multimodal data pipelines; and experience translating enterprise customer or Solutions Architect capability gaps into data collection requirements.
Required
- 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 within the AI/ML sector at a technology company
- 8+ years of direct people management experience
- Deep understanding of LLM/VLM architectures, training regimes, RLHF, and RLAIF
- Proven track record managing large-scale data pipelines and diverse modalities
- Experience working directly with enterprise customers or Solutions Architects to identify product capability gaps and translate them...
Preferred
- Experience releasing public datasets or driving data-as-a-resource initiatives that activate an external developer community
- Experience driving industry-wide standards for data formats and quality metrics
- Exposure to data management and labeling platforms such as Scale.ai or Labelbox
- Python, SQL, or Spark
Original job description
Content provided by the employer
Original job description
Content provided by the employer
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.
What You Will be doing:
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.
What We Need to See:
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.
Ways to Stand Out From the Crowd:
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.
NVIDIA is widely considered to be one of the technology world’s most desirable employers! We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 340,000 USD - 517,500 USD.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.About the company
NVIDIA
Large Enterprise
NVIDIA is a leading technology company renowned for its graphics processing units (GPUs) and innovative computing solutions that enhance visual experiences across multiple platforms, including gaming, scientific research, and artificial intelligence. Founded in 1993, the company has expanded its offerings to include powerful AI frameworks and deep learning platforms, making significant contributions to industries such as gaming, data centers, automotive, and healthcare. NVIDIA's commitment to pushing the boundaries of visual computing continues to drive advancements in both hardware and software, positioning the company at the forefront of emerging technologies and digital transformation.
NVIDIA is a leading technology company renowned for its graphics processing units (GPUs) and innovative computing solutions that enhance visual experiences across multiple platforms, including gaming, scientific research, and artificial intelligence. Founded in 1993, the company has expanded its offerings to include powerful AI frameworks and deep learning platforms, making significant contributions to industries such as gaming, data centers, automotive, and healthcare. NVIDIA's commitment to pushing the boundaries of visual computing continues to drive advancements in both hardware and software, positioning the company at the forefront of emerging technologies and digital transformation.