NVIDIA DGX Cloud is the AI supercomputing-as-a-service substrate designed to power the next generation of AI and industrial-scale breakthroughs. As a Security Data Engineer within our Infrastructure Security Engineering organization, you will build the data backbone of our security control plane—the pipelines, lake, and analytics that turn fragmented telemetry from a 250,000+ GPU fleet into a single, queryable, trustworthy picture of security state. Every posture score, every detection, and every autonomous action our platform takes stands on the data foundation you engineer.
What You Will Be Doing:
- Security Data Pipelines: Design, build, and operate the ingestion and transformation pipelines that collect security telemetry and asset inventory from dozens of heterogeneous sources, and normalize them into one canonical model.
- Data Lake & Lakehouse Engineering: Architect and run the storage layer. A data lake/lakehouse built on open formats, with the schema flexibility to absorb structured inventory, semi-structured telemetry, and unstructured logs without constant, breaking migrations.
- Security Analytics & Detection Engineering: Build the query and analytics layer that powers posture scoring, coverage and drift metrics, freshness monitoring, and multi-source correlation.
- Securing the Data Layer Itself: Treat the data platform as a high-value target, because it is. The data you store is a map of every host, every gap, and every credential path. You will engineer encryption at rest and in transit, fine-grained RBAC/ABAC, non-repudiable audit logging, data classification, network isolation, and verifiable retention and purge.
- Data Quality & Trust: Build for stable identity, source attribution, append-only history, and honest coverage. Make a source going quiet a finding, not silence, so that every downstream number comes with a known confidence.
- Multi-Functional Collaboration: Partner with the security control plane team, the inventory systems, identity and endpoint teams, and broader NVIDIA data and security organizations to define data contracts early, so these systems converge by design.
What We Need to See:
We truly recognize that a candidate who checks every single box is simply rare. We aren't looking for a checkbox hire; we are looking for high-caliber engineers with deep spikes of expertise in a few of these areas and the intellectual curiosity to dive into the rest. If your experience aligns with the core of this role—building data systems that are trustworthy at scale—and you can show us how, we want to hear from you!
- Data Engineering at Scale: 15+ years of experience designing, building, and operating production data pipelines, lakes, or lakehouses at high volume and throughput. You build systemic solutions rather than performing manual data wrangling or "tool administration." Bachelor's degree or equivalent.
- Production-Grade Coding: A strong software engineering background with the ability to write clean, maintainable, and well-tested code (e.g., Python, Go, Scala, SQL). You should be comfortable building and operating production data services at scale.
- Data Modeling & Schema Design: Proven ability to design canonical schemas and data models that span many disparate sources and evolve over time without breaking the consumers that depend on them.
- Distributed Data Systems: Hands-on experience with the modern data stacks, both streaming and batch processing, object storage, open table formats, and interactive query engines.
- Security-Minded Data Handling: You design data systems that are themselves defensible. Access control, encryption, audit, and isolation are first-class concerns in your work, and you understand that security data is among the most sensitive data an organization holds.
- Analytics Enablement: A track record of making large, messy datasets genuinely useful—serving interactive analysts, dashboards, and downstream services with data they can trust and query at low latency.
- Foundation: Bachelor's degree in Computer Science, Engineering, or a related technical field (or equivalent experience).
Ways To Stand Out from the Crowd:
- Security Telemetry & Detection Engineering: Experience building SIEM or data-lake detection content, normalizing security logs into common schemas (e.g., OCSF, ECS), or engineering the data layer that feeds correlation and anomaly-detection systems.
- Real-Time & Streaming Data: Expertise building low-latency, near-real-time pipelines where a correlation is only as fast as its slowest input, and detection is measured in minutes.
- HPC/AI Fleet Telemetry: Experience working with GPU and hardware telemetry (DCGM, Redfish/BMC, InfiniBand) or fleet-scale observability across hundreds of thousands of devices.
- AI-Ready Data: Experience engineering the data and feature layers that feed ML or LLM-based reasoning systems, enabling agents to correlate, predict, and act on trustworthy data. How have you made data safe to reason over?
NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High-Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars. NVIDIA is looking for great people like you to help us accelerate the next wave of artificial intelligence.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until June 13, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.