HPE
Lead Escalation Engineer (San Jose, CA)
$187K–$378K
San Jose, California, United States of America · Full-time · Posted Oct 28, 2026
- Experience
- 8+ years of relevant professional experience; qualifications also state a BS with 8+ years of relevant experience, or an MS with 6+ years.
- Education
- Bachelor of Science in Computer Science, Computer Engineering, Electrical Engineering or a related field; or MS.
About the job
Leads complex customer escalations for enterprise networking products, resolves root causes, and develops automation and AI-assisted workflows to improve diagnosis and network operations.
What you'll do
- Drive high-severity escalations through customer recovery, root-cause analysis, and permanent engineering solutions.
- Investigate network issues using telemetry, logs, packet captures, and lab reproduction; coordinate across Support, Development, QA, and Product Management.
- Build troubleshooting tools, automation, playbooks, and AI-assisted diagnostic workflows; mentor teams and turn recurring issues into product improvements.
What they ask for
- Strong troubleshooting and debugging experience with enterprise WLAN and networking systems, including Wi-Fi system-level testing and complex multi-component investigations.
- Expertise in Layer 2/Layer 3 networking, TCP/IP, IPv4/IPv6, OSPF, enterprise authentication, and Wi-Fi 6/6E; familiarity with Wi-Fi 7.
- Hands-on experience with Wireshark/tcpdump, Linux, virtualization, cloud environments, and cloud-managed, SaaS, or distributed systems.
- Experience with Python, REST APIs, JSON, scripting, automation, telemetry, and observability; working knowledge of Generative AI and AI-assisted engineering tools.
Nice to have
- Networking certifications such as CCNA, CCNP or CWNP are highly desirable.
Pay
Annual Salary USD 187,000 - 377,500 in California (base salary)
Tools
Wi-Fi 6/6E, Wi-Fi 7, TCP/IP, IPv4/IPv6, OSPF, Wireshark/tcpdump, Python, AWS/Azure
AI focus
Build AI-assisted diagnostic workflows and use Generative AI and LLM-based troubleshooting, log analysis, knowledge retrieval, case summarization, and RAG/agentic workflows to improve diagnosis and support increasingly autonomous network operations.
Model-written summary · the original posting is authoritative