Principal Engineer - Data Retrieval

NVIDIA•US, MA, Remote, WA

1d•$272,000 - $431,250

About The Position

NVIDIA is seeking an engineering lead to architect and scale the data acquisition infrastructure that powers its foundation models. Initially focused on code and STEM data for Nemotron, the role will build systems to discover valuable sources, crawl the open web at scale, and deliver reliable, high-quality data to training pipelines. This hands-on technical leadership position spans distributed systems, crawl infrastructure, and third-party data ingestion. The engineer will own the architecture of a unified platform covering source discovery, URL indexing, crawl frontiers, predictive ranking, scheduling, distributed fetching, and ingestion. Existing infrastructure will be brought to pretraining scale and extended to additional model families, modalities, and acquisition workflows. The role includes leading architecture reviews and critical implementation work, mentoring engineers, and helping build the team. The platform is a core capability for NVIDIA’s AI models and shapes the reach and quality of training data. The position works across engineering, research, product, storage and scaling, parsing and curation, legal, privacy, security, and data governance teams.

Requirements

BS, MS, or PhD in Computer Science, Computer Engineering, or a related technical field, or equivalent experience.
15+ years of relevant software engineering experience, 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, with the 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.

Nice To Haves

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.

Responsibilities

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, handles dynamic websites, and keeps 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, translating publisher preferences, opt-outs, licensing terms, and use restrictions into auditable controls that follow 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 connect source coverage and processing quality to controlled training experiments.
Coordinate dependencies across teams and make tradeoffs between immediate data delivery and long-term platform development.
Lead architecture reviews and critical implementation work, mentor engineers, and help build the team.