HPE
Senior Presales, Systems Engineer
$203K–$420K
Sunnyvale, California, United States of America · Senior · Full-time · Posted Oct 14, 2026
- Experience
- Minimum 10 years in a technical/pre-sales role, including 5 years as a Sales or Systems Engineer.
- Education
- Bachelor's and/or master’s degree in engineering, Computer Science, Information Technology, and or related field or equivalent experience.
About the job
Provides technical presales expertise for HPE Networking, designing and presenting networking and security solutions to customers and supporting sales through proposals, demonstrations, and partner engagement.
What you'll do
- Consult on the design, sale, and implementation of networking and security solutions; develop architecture designs, bills of materials, and implementation specifications.
- Partner with sales teams to assess customer needs and prepare responses to RFPs, RFQs, and RFIs.
- Present and demonstrate Juniper technologies, build customer and partner relationships, and deliver training and support.
What they ask for
- At least 10 years’ experience in a technical/presales role, including 5 years as a Sales or Systems Engineer.
- Bachelor’s and/or master’s degree in engineering, Computer Science, Information Technology, or a related field, or equivalent experience.
- Extensive experience designing and engineering LAN/WAN, Campus, Data Center, Security, and Cloud solutions, and implementing enterprise Campus and branch networks.
- Deep knowledge of routing protocols and extensive experience with datacenter network design and MPLS/WAN network design.
Nice to have
- Network security experience; experience with Paragon, MIST, Apstra, or equivalent products.
- AI networking, virtualization, DevOps automation, cloud technologies, or industry certifications such as JNCIP, JNCIE, CCNP, or CCIE.
Pay
USD 203,000 - 420,000 (combined base and target-level sales compensation; 75%/25% base and target-level mix)
Tools
Juniper, EVPN/VXLAN, MPLS, RoCEv2, DCQCN, UEC, Python, Ansible
AI focus
Preferred proficiency in AI networking technologies, including RoCEv2, DCQCN, UEC, and AI design options at scale.
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