DDN

• Act as the primary escalation point from Support to Engineering when customer issues cannot be resolved at lower support levels. • Perform deep technical analysis to identify root causes and provide timely, effective solutions whenever possible without engaging development teams. • Collaborate closely with Engineering when product defects or architectural changes are required, ensuring clear and actionable problem statements. • Maintain strong hands-on familiarity with VMstore products, architecture, and deployment models, and understand their role within customer data centers. • Diagnose complex issues involving storage, compute, networking, and virtualization layers. • Support and troubleshoot enterprise data center technologies with strict requirements for high availability, reliability, performance, and scalability. • Demonstrate strong familiarity with virtualization platforms, especially VMware, Platform9, and Hyper‑V. • Understand strengths, weaknesses, and trade-offs of different cloud technologies and hybrid deployment models. • Act as a strong advocate for customer needs within the organization, ensuring customer-impacting issues receive appropriate visibility and prioritization. • Maintain a high level of professionalism and clear communication in all customer interactions, particularly under high-stress escalation scenarios. • Provide clear guidance, workarounds, and best practices to customers and internal teams. • Apply strong Linux system familiarity to troubleshoot and resolve complex product and customer environment issues.

• Own and grow enterprise accounts within your territory • Create and convert net-new business opportunities • Lead complex, multi-stakeholder enterprise sales cycles from prospecting through close • Build relationships with executive buyers and technical influencers • Partner closely with technical teams to shape compelling AI and data infrastructure solutions

• Maintain, extend, and improve our FUSE module, and upstream changes as approporiate. • Design and develop in C/C++ under tight deadlines with minimal supervision • Define and articulate technical designs with the appropriate details • Full application development from design to development to testing to deployment • Participate in technical reviews throughout the course of development • Architecture and troubleshooting of system level components, especially the file system and related storage system components. • Undertake research based technical tasks: Define research approach, document results and Derive conclusions. Should be able to design a solution based on the research conducted • Mentor junior developers • Support product deployment and production defect resolution in a timely manner

• Build and optimize LLM serving and inference systems for production environments • Improve performance across GPU and CPU pathways • Work on KV cache, memory, storage, and throughput bottlenecks • Design and scale systems that support RAG and retrieval-heavy AI workloads • Contribute to infrastructure where storage architecture and systems efficiency materially affect AI performance • Solve engineering problems at the intersection of AI, high-performance systems, and distributed infrastructure

• Lead the design and implementation of high-performance data movement pipelines using NVIDIA NIXL across GPU, CPU, and storage tiers. • Architect and drive integration of DDN Infinia with GPU-accelerated inference platforms for large-scale, real-time AI workloads. • Own end-to-end optimization of I/O paths between GPU memory and storage using technologies such as NVIDIA GPUDirect Storage, RDMA, and NVMe-over-Fabrics. • Define and implement multi-tier storage architectures (NVMe, SSD, object storage) optimized for inference latency, throughput, and scalability. • Lead development of advanced KV cache management strategies, including offloading, prefetching, and persistence across distributed storage layers. • Partner with AI/ML engineering teams to optimize inference performance in frameworks such as PyTorch and TensorFlow. • Establish benchmarking frameworks and lead performance tuning efforts for storage and data movement in production inference environments. • Diagnose and resolve complex system bottlenecks across storage, networking, and GPU subsystems. • Influence architecture decisions for distributed inference systems, ensuring scalability, resilience, and efficient data locality. • Drive engineering excellence through best practices in observability, performance monitoring, automation, and reliability engineering. • Mentor junior engineers and provide technical leadership across cross-functional teams.

• Partner with customers to understand their critical data challenges—high-frequency trading, genomics processing, AI model training, or autonomous vehicle sensor fusion • Translate technical requirements into elegant, scalable storage solutions that exceed expectations • Act as trusted technical advisor throughout the sales cycle • Architect storage for AI/ML training, HPC simulations, data analytics, and GPU-accelerated workloads • Design for reliability, resilience, security, and scale—balancing performance with data protection • Create BOMs, system architectures, and technical proposals for complex RFPs • Conduct live demos, POCs, and performance benchmarking • Integrate with GPU clusters, Kubernetes, cloud platforms, and AI frameworks • Stay current on AI infrastructure and storage technology trends

• Act as a Strategic AI Client Director within the assigned West Coast territory, driving adoption of DDN’s AI infrastructure solutions across enterprise and research organizations. • Drive revenue growth and achieve or exceed assigned sales quota across the West Coast region. • Develop and execute strategic territory and account plans targeting organizations building large-scale AI and high-performance computing environments. • Identify and qualify new opportunities across industries including financial services, healthcare, telecommunications, technology, research institutions, and large enterprise organizations. • Build trusted relationships with senior technical leaders, AI infrastructure teams, and executive decision-makers within target accounts. • Partner with solutions architects and engineering teams to design and present AI infrastructure and high-performance data storage solutions. • Deliver compelling presentations, product demonstrations, and proposals showcasing the value of DDN’s AI-optimized storage platforms. • Manage the full enterprise sales cycle including pipeline development, forecasting, contract negotiation, and deal closure. • Develop strong relationships with channel partners, system integrators, OEM partners, and distributors to expand market reach. • Stay current on market trends related to AI infrastructure, GPU computing, large-scale model training, and high-performance data environments. • Represent DDN at industry events, conferences, and customer engagements to generate new opportunities and strengthen market presence.

• Provide end-to-end program oversight across all product lines • Be the connective tissue between Engineering, Product Management, QA, Support, and Operations • Ensure that programs move on schedule, risks surface early, and stakeholders have clear, consistent visibility • Own the master program schedule across TxOS, TGC, and Cloud Engine product lines • Drive cross-functional program reviews, stand-ups, and milestone checkpoints • Maintain and publish program status dashboards in Jira and Confluence • Identify, track, and mitigate program risks and dependencies across teams • Manage the Product Lifecycle (PLC) gate process • Partner with Engineering leads to ensure sprint commitments roll up accurately to release timelines • Own release readiness: GA checklists, RTM criteria, Beta entry/exit • Facilitate retrospectives and drive action items through to closure • Coordinate with Manufacturing, Support, and Field teams on release readiness and logistics • Drive adoption and compliance with the new Atlassian SDLC toolchain • Produce executive-level program summaries on a weekly cadence • Manage escalation paths and ensure critical blockers reach the right decision-maker fast

• The Support Account Manager (SAM) is the named, dedicated, post-sales relationship owner for a portfolio of Tintri's strategic enterprise customers. • The SAM is responsible for the customer's day-to-day experience of Tintri Support, the proactive health of their install base, the timely resolution of escalations, and the documented relationship that keeps the account healthy across multi-year contract cycles. • The SAM works in close partnership with the customer's primary technical contacts, executive sponsors, Tintri Engineering, Tintri Support, and Sales (Regional Sales Manager, Account Executive, Sales Engineer). • The role carries a portfolio of approximately five to eight strategic accounts, typically Platinum-tier or above, in regulated industries (financial services, healthcare, public sector, large enterprise). • The SAM owns the customer's stewardship outcome.

• Lead, mentor, and develop the Sales Engineering team across the Americas • Define regional SE strategy aligned with global go-to-market objectives • Establish best practices for discovery, solution design, POCs, and technical close • Partner with Sales leadership to support pipeline growth, deal strategy, and forecast accuracy • Serve as an executive technical sponsor for strategic and high-value customer engagements • Provide escalation support for complex deals, competitive situations, and executive presentations • Ensure consistent technical messaging and architectural standards across the region • Act as the voice of the field to Product Management and Engineering, influencing roadmap priorities • Partner with Marketing and Enablement teams on messaging, demos, and technical training • Work closely with Professional Services and Support to ensure seamless customer transitions post-sale • Recruit, onboard, and retain top Sales Engineering talent • Set performance goals, coach career development, and conduct performance reviews • Support capacity planning, regional coverage models, and resource alignment • Drive operational rigor through metrics, reporting, and continuous improvement