NVIDIA is seeking an engineering lead to architect and scale the data acquisition infrastructure that powers our foundation models. Starting with code and STEM data for Nemotron, you will build the systems that discover valuable sources, crawl the open web at scale, and deliver reliable, high-quality data to training pipelines. This is a hands-on technical leadership role spanning distributed systems, crawl infrastructure, and third-party data ingestion. You will own the architecture of a unified platform covering source discovery, URL indexing, crawl frontiers, predictive ranking, scheduling, distributed fetching, and ingestion. We have existing infrastructure to build on, and the goal is to bring it to pretraining scale and extend it to additional model families, modalities, and acquisition workflows over time. Along the way, you will lead architecture reviews and critical implementation work, mentor engineers, and help build the team.
What you’ll be doing:
- Work with storage and scaling teams to design scalable frontier storage and queueing systems with high-throughput writes, efficient lookups, deduplication, and scheduling based on data quality, diversity, and freshness. Build a continuous discovery loop that extracts links and feeds new candidates back into the crawl frontier.
- Develop a shared crawl execution layer that fetches reliably and politely at scale, handling dynamic websites while keeping resource use under control.
- Build extensible pipelines for open-web, licensed, and partner content, including text, PDFs, code repositories, and multimodal assets. Define stable interfaces for source connectors so teams can add sources without rebuilding the platform.
- Partner with parsing and curation teams to preserve valuable code, math, and structured content, and improve accepted data yield.
- Implement provenance and policy enforcement with legal, privacy, security, and data governance partners. Translate approved publisher preferences, opt-outs, licensing terms, and use restrictions into auditable controls that follow the data downstream.
- Own production readiness, including capacity planning, observability, fault tolerance, recovery, and cost efficiency. Instrument the pipeline end to end and set service objectives for throughput, freshness, reliability, and meeting training cutoffs.
- Work with research and product teams to evaluate internal crawls and external datasets through comparable pilots. Deliver reproducible data releases that tie source coverage and processing quality to controlled training experiments.
- Coordinate dependencies across teams and make clear tradeoffs between immediate data delivery and long-term platform development.
What we need to see:
- A BS, MS, or PhD in Computer Science, Computer Engineering, or a related technical field, or equivalent experience.
- 15+ years of relevant experience in software engineering, including designing and operating large-scale distributed systems, setting technical direction, and delivering complex infrastructure across multiple teams.
- Deep experience with web crawling or closely related discovery, indexing, or ingestion infrastructure, including distributed scheduling, stateful services, storage, and high-throughput data processing.
- Strong hands-on engineering and system design skills. Ability to move between architecture, implementation, performance analysis, and production debugging.
- Experience building batch or streaming pipelines and reasoning about consistency, retries, duplicate processing, backpressure, and recovery. A track record of improving throughput, latency, resource efficiency, or operational reliability.
- Experience delivering data platforms for research, ML, search, or other demanding technical users. Ability to translate evolving requirements into clear interfaces and measurable engineering outcomes.
- Strong technical communication and leadership through influence across engineering, product, research, and governance teams.
Ways to stand out from the crowd:
- Experience architecting a web-wide crawl platform, scaling crawl frontier storage, or consolidating multiple acquisition systems into shared infrastructure.
- Direct experience supplying foundation-model pretraining data, including targeted discovery, quality-aware crawl ranking, and acquisition of code or technical content.
- Experience implementing publisher consent, training-use opt-outs, dataset licensing controls, or provenance infrastructure at scale.
- Background with browser-based or agentic data collection, programmable extraction, and sandboxed environments for tool-use data or post-training evaluation.
- Experience onboarding licensed or multimodal datasets, building reusable partner integrations, or delivering streaming feeds with low latency for retrieval and grounding.
The platform is a core capability for NVIDIA’s AI models and directly shapes the reach and quality of our training data. If you enjoy solving difficult distributed systems problems and building platforms that other teams depend on, we want to hear from you.
