# Senior Manager, Training Data at NVIDIA

NVIDIA is hiring a Senior Manager, Training Data in Santa Clara, United States. It pays $240k-$380k a year and Level rates it Works on AI ●●●○; you can [apply on NVIDIA](https://jobsbylevel.com/go/a93c892f-747f-4e72-b120-e1f14c26d103?utm_medium=ai-agent).

AI Level 3, AI centrality 65 out of 100. US, CA, Santa Clara.

## Details

- Company: [NVIDIA](https://jobsbylevel.com/companies/nvidia)
- AI level: AI Level 3 (score 65 out of 100)
- Location: US, CA, Santa Clara
- Salary: $240k-$380k
- Posted: October 28, 2026
- Apply: https://jobsbylevel.com/go/a93c892f-747f-4e72-b120-e1f14c26d103?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-manager-training-data-at-nvidia-003ff2

## Description

NVIDIA is seeking a senior product leader to define the strategy and build the platforms that deliver high-quality training data for our foundation models. Starting with web and code data for Nemotron, you will help research teams discover valuable new sources from the open web, improve data quality and diversity, and turn raw content into datasets that advance model capabilities. In this founding product leadership role, you will partner with an engineering lead and work across research, data acquisition, data platforms, and legal teams to build the roadmap and investment priorities. You will connect data acquisition and curation decisions to measurable training outcomes, balancing quality, scale, delivery time, and cost. As research needs evolve, you will extend the platform to additional domains and modalities! This role offers the opportunity to shape a foundational capability for NVIDIA’s AI models, working directly with researchers and engineers to improve what our models can learn. If you bring deep technical curiosity, product judgment, and a record of delivering ambitious data platforms, we want to hear from you. What you’ll be doing: Own the product vision, strategy, and roadmap for training data acquisition and preparation. Translate research priorities and model capability gaps into source requirements, platform investments, and dataset acceptance criteria aligned with training schedules. Define requirements for source discovery, URL indexing, predictive quality and diversity ranking, crawl scheduling, distributed fetching, and ingestion. Partner with engineering on throughput, reliability, storage, and capacity planning as workloads scale. Drive improvements in parsing and curation, including HTML extraction, browser-rendered content, code and math preservation, duplication, quality filtering, and dataset composition. Identify where valuable content is lost and increase the yield of usable, differentiated data. Lead build, buy, and partner decisions across internal crawling, commercial datasets, and licensed content. Establish comparable internal and vendor pilots that measure incremental coverage, accepted data yield, delivery reliability, and total cost against existing corpora. Partner with researchers on controlled training experiments and ablations to measure data impact on model capabilities. Account for benchmark contamination, dataset composition, and regressions, and use results to guide source selection and acquisition investment. Work with legal, privacy, policy, and security teams to translate approved requirements into scalable controls for source eligibility, access restrictions, opt-outs, provenance, and permitted use. Define auditable dataset acceptance and release processes. Build researcher-facing capabilities for dataset discovery, metadata, versioning, lineage, and quality reporting. Enable reproducible dataset releases and reuse, measure adoption, and reduce the time from a research request to training-ready data. Align teams on priorities, dependencies, and ownership. Shape hiring and resource plans with engineering leadership, communicate investment tradeoffs to senior leaders, and establish shared metrics for model impact, data coverage, pipeline latency, cost, and delivery against training cutoffs. What we need to see: A BS or MS in Computer Science, Engineering, or a related technical field, or equivalent experience. 8+ overall years in product management or a related technical role, including 5+ years on data platforms, search/indexing, or ML infrastructure. 4+ years direct people management experience A track record of delivering data systems at scale and improving quality, reliability, processing time, adoption, or cost. Experience building a new platform or taking a technically complex product from concept to production. Strong technical understanding of distributed data pipelines, storage, indexing or retrieval, and data processing. Ability to engage in architecture and capacity

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/senior-manager-training-data-at-nvidia-003ff2

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