Data Acquisition Platform Architect

NVIDIA Corporation; United States; On-site; USD 250,000 - 450,000; Full time

Job summary

NVIDIA Corporation is seeking an engineering lead to architect and scale our data acquisition infrastructure powering foundation models. You will own the unified platform for source discovery, crawling, indexing, and ingestion across multimodal data.

Lead architecture reviews, mentor engineers, and guide cross-functional teams to deliver scalable, reliable data pipelines at pretraining scale and beyond.

Qualifications

• BS, MS or PhD in Computer Science, Computer Engineering, or related field (or equivalent).
• 15+ years of software engineering experience building large-scale distributed systems.
• Deep experience with web crawling, indexing, ingestion infrastructure, and high-throughput data processing.

Responsibilities

• Design scalable frontier storage and queueing systems with high-throughput writes and efficient lookups.
• Develop a shared crawl execution layer that fetches at scale while respecting resource limits.
• Build extensible pipelines for open-web, licensed, and partner content including multimodal data.
• Collaborate with parsing and curation teams to preserve valuable data and improve yield.
• Implement provenance, policy enforcement, and auditable controls with governance partners.
• Own production readiness: capacity planning, observability, fault tolerance, and cost efficiency.
• Evaluate internal/external datasets through pilots and deliver reproducible data releases.
• Coordinate cross-team dependencies and tradeoffs between immediate delivery and long-term platform goals.

Skills

Distributed systems

Web crawling

System design

Production debugging

Technical leadership

Data platforms

Education

BS/MS/PhD in Computer Science or related field

Job description

NVIDIA Corporation is seeking an engineering lead to architect and scale our data acquisition infrastructure powering foundation models. You will own the unified platform for source discovery, crawling, indexing, and ingestion across multimodal data.

Lead architecture reviews, mentor engineers, and guide cross-functional teams to deliver scalable, reliable data pipelines at pretraining scale and beyond.