Drimva

Cisco

Engineering Product Manager

$179K–$329K

San Jose, California, US +2 · Hybrid · Manager · Full-time · Posted Oct 28, 2026

Location
San Jose, California, US
San Francisco, California, US
New York, New York, US
Experience
12 years with a bachelor's degree, 8 years with a master's degree, or equivalent practical experience.
Education
Bachelor's degree plus 12 years, master's degree plus 8 years, or equivalent practical experience.

About the job

Owns the strategy, development, launch, and ongoing improvement of an AI infrastructure product for cloud operators, enterprise platform teams, and AI developers.

What you'll do

  • Define product vision, strategy, roadmap, requirements, and phased releases based on customer discovery.
  • Lead cross-functional product decisions and coordinate teams and partners through delivery and launch.
  • Track product and operational metrics, and own market analysis, business cases, pricing input, and go-to-market readiness.

What they ask for

  • Bachelor's degree plus 12 years, master's degree plus 8 years, or equivalent practical experience in product management, engineering, or a related technical field.
  • Experience owning an enterprise infrastructure, cloud, or platform product from discovery through operation, and creating a product from the ground up.
  • Deep expertise in at least one of Kubernetes, networking, storage, or security, with working fluency across at least two of the remaining domains.
  • Experience defining technically complex requirements and influencing engineering and business leaders.

Nice to have

  • Experience operating production cloud or Kubernetes environments.
  • Fluency in AI infrastructure topics, such as GPU clusters, accelerator scheduling, model serving, inference, and infrastructure telemetry.

Pay

USD $194,600.00 to $285,700.00 (salary range); USD $194,600.00 - $328,600.00 (salary range, New York City Metro Area); USD $179,000.00 - $294,000.00 (salary range, non-metro New York state & Washington state)

Tools

Kubernetes, networking, storage, security, APIs, GPU clusters

AI focus

Builds AI infrastructure product capabilities for managing infrastructure used by AI workloads, including GPU clusters, accelerator scheduling, workload placement, model serving, inference, and telemetry.

Model-written summary · the original posting is authoritative

First seen Oct 29, 2026 · Last verified Nov 4, 2026 (UTC)