Senior Product Manager (AI Datacenter)

Keysight Technologies, Inc.•Santa Clara, CA

About The Position

Keysight is seeking a Senior Product Manager to define and drive the vision, strategy, and roadmap for the Keysight AI Data Center Network Validation Solution, which helps customers validate and benchmark networks connecting AI clusters. The role combines networking expertise with knowledge of the AI Data Center market and technologies, using market trends, customer insights, and cross-functional collaboration to develop high-impact products. The position owns the validation solution’s product strategy, roadmap, and lifecycle for AI Data Center Scale-X networks. It gathers and prioritizes customer and partner requirements, translates them into product documentation and development priorities, and partners with engineering and quality teams to deliver products. The role also tracks industry standards and market developments, develops business cases and go-to-market plans, leads launches and sales enablement, and measures product performance and adoption. The listed California pay range is $193,840–$323,070; pay varies by experience, education, skills, and location. The role is eligible for the Keysight Results Bonus Program. US employees may be eligible for the benefits listed in the posting.

Requirements

Bachelor’s or Master’s degree in Computer Science, Electrical or Software Engineering, or a related field, or equivalent experience.
7+ years of relevant networking experience, including product management or product ownership exposure.
Solid networking background, including Ethernet switching, IP routing, and high-speed Ethernet.
Working knowledge of data center infrastructure, leaf-spine topologies, SmartNICs, and network operating systems.
Understanding of the AI data center market and Scale-X networking technologies such as RDMA, RoCEv2, and Ultra Ethernet.
Exposure to product strategy, roadmaps, and market analysis.
Strong analytical, problem-solving, and decision-making skills, with the ability to balance technical depth and business priorities.
Excellent written, verbal, and presentation skills, including the ability to synthesize complex technical and market data for technical and executive audiences.
Self-driven, curious, and motivated to continuously learn emerging high-speed Ethernet and AI networking technologies.

Nice To Haves

MBA or equivalent.
Some understanding of AI workloads, including training, inference, and collective communication, and performance benchmarking metrics.
Experience in the network test industry and a working understanding of the test and measurement market, including how network test and validation tools are used.
Strong public speaking and writing skills; contributions to industry standards and forums.

Responsibilities

Own the product strategy, roadmap, and lifecycle of the validation solution for AI Data Center Scale-X networks, aligned with business objectives and market needs.
Gather and prioritize requirements from hyperscalers, network equipment manufacturers, NIC/silicon vendors, and AI cluster operators; translate them into PRDs, user stories, and acceptance criteria.
Manage the product backlog and make trade-off decisions with R&D across hardware and software releases.
Partner with R&D, Software Engineering, and Quality on technical feasibility, release scope, and high-quality, on-time delivery; resolve technical questions and remove roadblocks throughout development.
Track and influence emerging AI networking standards and industry benchmarks, including the Ultra Ethernet Consortium, Ethernet Alliance, OCP, IEEE 802.3, and MLPerf.
Analyze market trends and competitor offerings to shape the roadmap, product positioning, and differentiation.
Develop business cases, pricing, and go-to-market plans for new products and features with Marketing, Sales, and cross-functional teams.
Lead product launches and enable sales and pre-sales teams through technical messaging, demos, training, and competitive positioning.
Define success metrics, track product performance and customer adoption, and use data and customer feedback to drive continuous improvement.