Principal Product Manager, ExaScaler

About the company

DDN provides high-performance data intelligence and storage platforms that enable rapid, low-latency, high-throughput data access for NVIDIA GPU clusters, accelerating AI, LLM, GenAI, RAG, and HPC workloads.

About this role

DDN is seeking a Principal Product Manager to lead strategic product areas for EXAScaler. This role shapes product direction, drives cross-team alignment, and represents the product with senior customers and internal stakeholders. KEY RESPONSIBILITIES - Define and drive the multi-release strategy and roadmap for major EXAScaler product domains. - Translate strategy into prioritized, outcome-driven roadmaps, clear PRDs, and detailed user stories with measurable success criteria. - Partner with engineering leadership to make architecture, investment, and sequencing decisions, balancing innovation with reliability and technical debt reduction. - Act as a senior product voice with customers and partners, including executive briefings, roadmap deep dives, and joint solution planning for large-scale AI and HPC deployments. - Shape competitive strategy for EXAScaler in AI and high-performance data infrastructure, including pricing and packaging input, win/loss analysis, and market differentiation. - Use data (product analytics, customer feedback, and financial performance) to drive portfolio-level decisions and product investment trade-offs. QUALIFICATIONS MUST HAVE - 12+ years of product management experience in infrastructure, data platforms, storage, HPC environments, or cloud services. - Proven track record of delivering production features at scale, from definition through launch and iteration. - Demonstrated ability to write concise PRDs and user stories with clear acceptance criteria and measurable success metrics. - Experience working closely with sales teams, solutions architects, and customers on proof-of-concepts, roadmap discussions, and product escalations. - Excellent communication and stakeholder management skills across both technical and non-technical audiences. - Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent practical experience. NICE TO HAVE - Experience with data infrastructure supporting AI workloads, including training and inference pipelines or large-scale unstructured data environments. - Familiarity with parallel file systems and high-performance storage architectures. - Background in cloud-native architectures including microservices, containers, observability frameworks, and APIs. - Strong technical depth in at least one of the following: - Distributed storage or parallel file systems (Lustre, object, file, block, or key-value storage) - Cloud infrastructure platforms (AWS, Azure, GCP) or Kubernetes-based environments - Prior experience working in a B2B enterprise infrastructure company or high-growth technology environment.