Description

Define the product strategy, vision, and roadmap for GPU instances, clusters, and cloud services.

Align product positioning, requirements, and priorities with customer needs, market trends, and business objectives.

Manage the full product lifecycle, from initial planning and launch through ongoing optimization and eventual end-of-life.

Develop business cases, financial models, pricing strategies, profitability analyses, and TCO models to support product investments and decisions.

Develop and execute go-to-market strategies, including product messaging, positioning, launch plans, and customer engagement in partnership with marketing, sales, and solutions engineering.

Partner with GPU technology and ecosystem providers to align roadmaps, integrations, and technical requirements.

Translate AI, HPC, graphics, and other accelerated-computing workloads into product specifications, performance requirements, and technical architectures.

Guide the evolution of GPU infrastructure and make data-driven decisions around platform investments and lifecycle management.

Represent the needs of customers, engineers, and data scientists by identifying opportunities to improve usability, automation, monitoring, support, and maintenance processes.

Build strong relationships with engineering teams and secure alignment around product goals, technical requirements, and future GPU capabilities.

What We’re Looking For

12+ years of relevant product management, technology, or engineering experience and a bachelor's degree in computer science, Engineering, or equivalent experience.

Strong technical understanding of GPU architectures, CUDA, and accelerated computing platforms.

Experience with GPU resource management and cluster orchestration for AI and high-performance computing workloads.

Knowledge of cloud networking, GPU interconnects, infrastructure redundancy, and large-scale GPU deployments.

Experience developing both technical and business models for GPU/cloud products, including pricing, profitability, and Total Cost of Ownership (TCO) analysis.

Understanding of AI workload patterns, enterprise security requirements, and hardware-level APIs related to GPU infrastructure.

A strong customer-first mindset, with a focus on automation, usability, and low-friction integration for GPU workloads.

Proven ability to collaborate with highly technical engineering and data science teams and gain buy-in for new product initiatives.

Strong communication, strategic thinking, and stakeholder management skills.

Ability to balance complex technical requirements with customer needs and business objectives

Key Skills

Education

Bachelor's degree