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Distinguished Engineer - AI Data Engine

Job in San Jose, Santa Clara County, California, 95199, USA
Listing for: NetApp, Inc.
Full Time position
Listed on 2026-06-22
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 250000 USD Yearly USD 250000.00 YEAR
Job Description & How to Apply Below

About the Team

The Net App AI Data Engine team builds the end-to-end, storage-integrated AI data service that powers enterprise GenAI, RAG, and agentic AI  platform spans intelligent data discovery and curation, policy-driven governance, real-time vectorization, and high-performance retrieval endpoints — all deeply integrated with Net App ONTAP and our hybridmulticloudstorage estate.

We are moving fast, tackling problems at the intersection of AI and data infrastructure that no one else has solved at enterprise scale. This is a team where world‑class engineers do the most important work of their careers.

The Opportunity

We are looking for a Distinguished Engineer to join the Net App AI Data Engine team. This is the highest individual contributor engineering role at Net App — a title reserved for engineers who have demonstrated exceptional technical leadership, deep systems expertise, and meaningful industry impact.

In this role, you will set the technical direction for the AI Data Engine platform. You will architect and build core capabilities at the intersection of AI and data infrastructure — owning the hardest engineering problems across GenAI pipelines, intelligent data services, AI‑native storage integration, and agentic AI systems. You are expected to be a hands‑on builder, not a consultant. Distinguished Engineers at Net App write code, design systems, and serve as a role model for the broader engineering community.

What

You’ll Build and Lead
  • Architect, design, and implement core platform capabilities for the AI Data Engine — including data ingestion and curation pipelines, vectorization services, semantic search infrastructure, and retrieval endpoints for GenAI and agentic AI applications.
  • Define the technical architecture for AI‑native storage integration, ensuring Net App ONTAP and cloud storage surfaces are first‑class citizens in enterprise AI workflows.
  • Lead the design of highly available, fault‑tolerant, and horizontally scalable distributed systems that underpin the AI Data Engine platform.
  • Own end‑to‑end technical quality — from system architecture and API design to performance benchmarking, reliability engineering, and observability.
  • Partner with product management and design to shape the product roadmap and translate customer requirements into elegant, implementable technical solutions.
  • Drive cross‑team technical alignment across Net App engineering, influencing architectural decisions that span the AI Data Engine, ONTAP, cloud storage, and data services.
  • Evaluate and adopt emerging AI frameworks, model serving technologies, vector databases, and orchestration tools to continuously advance the platform.
  • Represent Net App in the external technical community — through patents, publications, standards bodies, and conference presentations.
  • Mentor and grow senior and principal engineers across the organization, elevating technical craft and engineering culture.
Education
  • 20+ years of software engineering experience, with a proven track record of technical leadership at scale in enterprise software, cloud infrastructure, or AI/ML platforms.
  • Deep expertise in distributed systems, cloud‑native architectures, and high‑performance data infrastructure — with a particular focus on AI/ML workloads.
  • Hands‑on experience building GenAI pipelines, RAG systems, vector stores, embedding infrastructure, or agentic AI frameworks.
  • Strong understanding of LLM serving, inference optimization, and the data infrastructure requirements for model training and fine‑tuning at enterprise scale.
  • Proficiency in Python and at least one systems programming language (Go, Rust, C++, or Java); experience with distributed compute frameworks (Spark, Ray, Dask) is a plus.
  • Experience with cloud‑native infrastructure (Kubernetes, cloud object storage, managed AI services on AWS/Azure/GCP) and storage systems (NFS, S3‑compatible, block storage).
  • Demonstrated ability to drive technical strategy, decompose complex problems into clean, scalable solutions, and hold the team to the highest engineering standards.
  • Excellent written and verbal communication skills; able to present complex technical concepts to both engineering audiences and…
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