Principal Engineer – Data Retrieval

NVIDIA

. Architect and scale data acquisition infrastructure powering NVIDIA’s foundation models, initially for code and STEM data for Nemotron.

Posted 10/27/2026full-timeRemote • SwitzerlandLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and scaling data acquisition infrastructure, with a strong focus on web crawling, distributed systems, and high-throughput data processing. Proven ability to lead cross-functional teams and deliver complex data platforms while ensuring compliance with legal and governance standards.

Highest-signal resume keywords

Web Crawling InfrastructureDistributed Systems DesignHigh-Throughput Data ProcessingTechnical CommunicationData Governance Compliance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringSystem DesignBatch Processing PipelinesStreaming PipelinesData IngestionDistributed SchedulingPerformance AnalysisProduction DebuggingData Platform DeliveryProvenance Infrastructure

Soft Skills

LeadershipTechnical CommunicationMentoringCross-Team CollaborationInfluence

Tools & Technologies

Crawl Frontier StorageData Acquisition SystemsObservability ToolsData Processing FrameworksQuality-Aware Crawl Ranking

Industry Keywords

Data GovernanceMachine LearningResearch Data PlatformsPublisher ConsentDataset Licensing

Tech Stack

Tools & technologies

Distributed Systems

About the role

Key responsibilities & impact

Architect and scale data acquisition infrastructure powering NVIDIA’s foundation models, initially for code and STEM data for Nemotron.
Design and own a unified platform for source discovery, URL indexing, crawl frontiers, predictive ranking, scheduling, distributed fetching, and ingestion.
Design scalable frontier storage and queueing systems with high-throughput writes, efficient lookups, deduplication, and quality-, diversity-, and freshness-based scheduling.
Build a continuous discovery loop that extracts links and feeds new candidates into the crawl frontier.
Develop a shared crawl execution layer that fetches reliably and politely at scale, including dynamic websites.
Build extensible pipelines for open-web, licensed, and partner content, including text, PDFs, code repositories, and multimodal assets.
Define stable source-connector interfaces.
Partner with parsing and curation teams to preserve code, math, and structured content and improve accepted data yield.
Implement provenance and policy enforcement with legal, privacy, security, and data governance partners.
Translate publisher preferences, opt-outs, licensing terms, and use restrictions into auditable downstream controls.
Own production readiness, capacity planning, observability, fault tolerance, recovery, and cost efficiency.
Instrument the pipeline end to end and set service objectives for throughput, freshness, reliability, and training cutoffs.
Work with research and product teams to evaluate internal crawls and external datasets through comparable pilots.
Deliver reproducible data releases connecting source coverage and processing quality to controlled training experiments.
Coordinate cross-team dependencies and tradeoffs between immediate data delivery and long-term platform development.
Lead architecture reviews, critical implementation work, engineer mentoring, and team building.

Requirements

What you’ll need

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.
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.
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.