AI Product Strategist Leader

AI Product Strategist Leader

Reports To: CEO

Company Overview:

Netlist, Inc. (OTCQB: NLST) is a leading provider of high-performance modular memory subsystems and next-generation storage-class memory solutions. We specialize in cutting-edge technologies that enable innovation in AI, cloud computing, and enterprise infrastructure. Our product portfolio includes DDR4/DDR5 DIMMs, NAND-based SSDs, and advanced interconnect technologies such as CXL and PCIe .

Join our world-class engineering team and be part of shaping the future of memory and storage architecture.

Position Summary:

This is a strategic, highly visible role responsible for identifying important and commercially meaningful problems in AI infrastructure and translating those problems into product opportunities.

The successful candidate will develop close relationships with architects, engineers, technology executives, and business leaders at hyperscalers, cloud service providers, server OEMs, and other companies deploying AI infrastructure.

You will investigate how AI workloads are evolving, understand the infrastructure challenges created by those workloads, and identify opportunities where Netlist Inc. can create differentiated products.

You will work closely with our Chief AI Systems Architect, AI Hardware / Memory Systems Architects, AI Inference / Systems Software Architects, engineering, and executive teams to evaluate market opportunities and translating them into product concept and a roadmap.

Key Responsibilities:

1. Customer Discovery & Market Intelligence

- Develop senior-level relationships with technology leaders at hyperscalers, cloud service providers, server OEMs, enterprise data centers, and other strategic customers.

- Meet regularly with AI infrastructure architects, system architects, platform engineers, product leaders, and data center executives to understand their current and future challenges.

- Conduct structured customer discovery to identify both known pain points and emerging problems that customers may not yet have fully articulated.

- Understand customers' AI infrastructure roadmaps, workload characteristics, deployment models, performance requirements, power constraints, and total cost of ownership.

- Determine which problems are technically significant, economically meaningful, and sufficiently urgent to justify a new product.

- Build and continuously maintain a database of customer requirements, pain points, technology trends, and competitive solutions.

2. AI Infrastructure Technology Strategy

- Maintain a strong understanding of the technologies driving the evolution of AI infrastructure, including:

- LLM and generative AI inference

- Agentic AI

- Long-context inference

- KV-cache and context memory

- AI accelerators and heterogeneous computing

- HBM and advanced memory architectures

- DDR and memory subsystems

- CXL memory expansion, pooling, and disaggregation

- AI networking and high-speed interconnect

- Near-memory computing and PIM

- Memory compression and intelligent data placement

- Edge AI and inference

- AI data center power and thermal challenges
- Track developments across hyperscalers, AI startups, academia, standards organizations, and the broader AI ecosystem.
- Develop an informed view of where AI infrastructure is heading over the next three to five years, and how those changes may create new product opportunities.

3. Product Opportunity Identification

- Convert customer pain points and technology trends into clearly defined product opportunities.

- Identify opportunities where the company's existing memory and systems expertise can provide a meaningful competitive advantage.

- Work closely with the AI Systems Architect to evaluate alternative technical approaches.

- Develop preliminary product concepts, target customers, use cases, value propositions, and differentiation.

- Determine which opportunities warrant further technical investigation and which should be discarded.

- Look for opportunities spanning both the company's existing markets and adjacent AI infrastructure markets.

4. Customer-Backed Product Definition

For each significant opportunity:

- Define the target customer and specific use case.

- Quantify the customer's current pain in terms of performance, latency, capacity, power, utilization, infrastructure cost, or TCO.

- Determine how the customer addresses the problem today.

- Define the desired solution and minimum requirements.

- Determine what performance or economic improvement would justify customer adoption.

- Develop an initial product specification together with the architecture team.

- Engage prospective customers early to validate the concept before substantial R&D investment.

5. Product Roadmap

- Build and maintain the company's AI product and technology roadmap.

- Develop a portfolio of near-term, mid-term, and longer-term opportunities.

- Prioritize opportunities based on customer value, market potential, differentiation, technical feasibility, time to market, and required investment.

- Develop business cases and investment recommendations for selected opportunities.

- Balance extensions of the existing memory business with larger adjacent-market opportunities.

- Present product recommendations and roadmap proposals to executive management.

6. Cross-Functional Product Development

- Work closely with:
- AI Systems Architect
Define the overall system architecture and technical approach.
- AI Hardware / Memory Systems Architect
Determine how memory, hardware, bandwidth, power, capacity, and cost requirements can be optimized.
- AI Inference / Systems Software Architect
Determine how software, AI models, and inference runtimes should utilize the architecture.
- Engineering
Translate product requirements into prototype and production designs.
- Sales / Business Development
Develop customer engagement and commercialization strategies.
- Executive Management
Align product investments with corporate strategy and financial objectives.

7. Competitive Intelligence

- Maintain a detailed view of existing and emerging competitors.

- Analyze competitor products, architectures, technology roadmaps, pricing, partnerships, and target markets.

- Identify gaps where customers remain underserved.

- Monitor emerging startups and technologies that could become either competitors, partners, investment opportunities, or acquisition targets.

- Develop recommendations for how [Company Name] can differentiate.

8. Strategic Partnerships, Investments & M&A

- Identify startups, technology companies, research organizations, and intellectual property relevant to the company's AI strategy.

- Evaluate potential technology licensing, strategic investment, partnership, and acquisition opportunities.

- Work with corporate development and executive management to evaluate strategic targets.

- Maintain a prioritized watchlist of emerging technologies and companies.

- Participate in technical and commercial due diligence for potential investments and acquisitions.

Required Qualifications:

- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field; MBA or advanced technical degree is a plus.

- 10+ years of experience in semiconductor, data center, AI infrastructure, computing systems, product management, product strategy, or a closely related field.

- Experience working directly with hyperscalers, cloud service providers, server OEMs, enterprise infrastructure customers, or semiconductor companies.

- Strong understanding of modern AI infrastructure and data center architectures.

- Ability to engage credibly with highly technical customers and understand architecture-level discussions.

- Demonstrated experience identifying customer needs and translating them into successful technology products.

- Strong analytical and business judgment.

- Excellent written, verbal, and presentation skills.

- Demonstrated ability to work effectively across engineering, product, sales, business development, and executive organizations.

Preferred Qualifications:

Experience with several of the following:

- Data Center AI/ML infrastructure

- LLM inference

- Generative AI

- AI accelerator architecture

- GPU systems

- HBM

- DDR

- CXL

- PCIe / UCIe

- Memory pooling and disaggregation

- AI networking

- AI storage

- KV-cache infrastructure

- Near-memory computing / PIM

- AI inference optimization

- Edge AI

- Data center architecture

- Technology strategy

- Strategic partnerships

- Venture investment or M&A

Salary Range: $325k