Technical Staff, Engineering Technologist
Dell Technologies · Santa Clara, CA · Engineering Technologist
About this role
Dell Technologies is hiring a staff-level Technical Lead in the software engineering function based in Santa Clara, CA. The posting calls out experience with Kubernetes, TensorFlow, PyTorch, Networking. Compensation is listed at $263,000–$341,000 per year.
- Role
- Technical Lead
- Function
- software engineering
- Level
- staff
- Track
- Tech leadership
- Employment
- Full-time
- Location
- Santa Clara, CA
- Department
- Engineering Technologist
- Posted
- May 13, 2026
More roles at Dell Technologies
Job description
from Dell Technologies careersTechnical Staff-Network Architect
From applied research to advanced engineering, the Engineering Technologist team has the expertise to
shape ground-breaking products, material and processes. It’s a fascinating field of work. We’re involved in
assessing the competition, developing technology and product strategies and generating intellectual
property. We lead technology investigations, analyze industry capabilities and recommend potential
acquisitions or vendor partner opportunities. Our insights influence product architecture and definitions.
And we work with colleagues across the business to ensure our products always lead the way.
Join us to do the best work of your career and make a profound social impact as a Technical Staff-
Network Architect on our Engineering Technologist Team in Santa Clara, California
What you’ll achieve
Participate in the development of next-generation large-scale AI Infrastructure to include accelerated
compute, AI Fabric and AI optimized storage. Engage with high profile AI customers to optimize solutions
for their applications and tune systems for maximum performance. Drive innovation at datacenter level
with liquid cooling technologies and power density.
You will:
Drive Network and Fabric design based on customer requirements and required infrastructure. Address
InfiniBand, Ethernet, Accelerated Fabric Link (AFL) and other Fabric topologies that scale based on
datacenter requirements. Define Network topologies that are optimized for Training and Inferencing.