below marketmost similar roles pay hereabove market
This role pays more than
85%
of similar roles. Most pay
$149,500–$198,431
— the blue band above.
At the midpoint, this role pays about
$219k
versus about
$174k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
1623 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1110 roles with salary data.
The Senior Software Engineer - Enterprise Data joins the team responsible for building the company's Enterprise Data Governance platform. This role involves owning the roadmap for sensitive-information detection, driving production rollouts for governed data access across systems like Outlook, Teams, Slack, Confluence, OneDrive, SharePoint, and Google Drive, and integrating content into AI knowledge platforms. The engineer will design export-control enforcement, audit logging, and RBAC controls while evaluating ML-based classification models for unstructured data. Key requirements include a degree in Computer Science or a related field, 8+ years of experience in large-scale enterprise platforms, and expertise in backend systems, distributed systems, and data engineering. Candidates must demonstrate skills in data connectors, indexing pipelines, and training ML models for security and information governance domains.
What does a Software Engineer earn in California?
Median $214500 from 1193 postings across 76 companies.
Own the roadmap for sensitive-information detection and remediation workflows with Finance, Legal, and Security teams.
Integrate ML-based classification approaches to detect sensitive content across unstructured data at scale.
Drive production rollout and self-service onboarding for the enterprise data access platform across various communication and document systems.
Design and implement export-control enforcement, audit logging, data masking, and RBAC controls across all connected systems.
Integrate enterprise content sources into the AI knowledge platform to ensure accurate and authorized access for AI agents.
Set and enforce quality bars for content ingestion, including freshness, accuracy, and access-control correctness.
Serve as technical lead by making architectural calls, resolving ambiguity, and providing mentorship through code and design reviews.
What we're looking for
Bachelor's or Master's Degree in Computer Science, Computer Engineering, or a related field (or equivalent experience).
8+ years of experience building and operating large-scale enterprise platforms with demonstrated growth in technical scope and ownership.
Strong foundation in backend systems, distributed systems, and data engineering, including large-scale data processing and indexing pipelines.
Experience building or integrating data connectors or enterprise SaaS integrations like Confluence, SharePoint, Google Drive, Slack, or Teams.
Experience training and evaluating ML models for classification tasks in security, content sensitivity, or information governance domains.
Strong communication skills to translate complex technical tradeoffs into clear recommendations for non-technical stakeholders.
Familiarity with access control models, remediation workflows, and audit requirements in enterprise security or compliance contexts (preferred).
Experience with AI/LLM data pipelines, vector stores, or RAG architectures, especially where access control fidelity is a hard requirement (preferred).
Distributed Systems
Backend Systems
High-Performance Computing
Data Governance
RBAC
API
Databricks
RAG
Vector Stores
LLM
DLP
Data Masking
Schema Design
Audit Logging
SaaS Integration