Director of Product Management

Join Tensordyne as the Director of Product Management, where you will lead the development of next-generation AI inference compute products. You will engage with external technology partners, customers, and internal stakeholders to shape the company's go-to-market strategy. Your responsibilities will include competitive analysis, key metrics tracking, and overseeing key customer projects. You will also work closely with marketing to promote Tensordyne's AI technology and products. The ideal candidate will have a strong background in engineering, product management, and business development.

• Defining and leading the development of Tensordyne's next generation of AI inference compute products, including creating a technology roadmap and shaping the company's go-to-market strategy.
• Engaging with external technology partners, customers, AI industry researchers, and internal stakeholders to gather insights and requirements, and driving competitive analysis and product release schedules.
• Overseeing and ensuring the success of key customers' projects, anticipating their software and hardware needs, and working closely with marketing to design campaigns promoting Tensordyne's AI technology.

• To keep developing world-class AI Inference compute systems (from silicon to software), we're looking for an experienced (Sr.) Director of Technical Product Management who is passionate and deeply knowledgeable about Al inference compute in datacenters, across the entire stack (silicon, hardware and software)
• The successful candidate will have a years-long background in engineering enabling them to have a profound technical understanding of most if not all dimensions of the product, along with experience in product management, business development, great communication and networking skills. If that's you, then we'd love to talk to you!
• We do not expect candidates to cover 100% of the list below - it depicts nevertheless all areas that eventually will require them to gain deep understanding in for them to ultimately become fully effective in this role
• Modern AI inference system KPIs, performance metrics, Pareto optimization, and efficiency trade-offs
• Deep understanding of the execution flow of modern AI inference systems, particularly large-scale Mixture-of-Experts (MoE) models, and world models
• Multi-user and multi-agent inference scheduling
• Demonstrated ability to connect low-level hardware capabilities with system-level performance and customer business outcomes
• Comprehensive understanding of AI compute infrastructure, including:
• High-performance networking topologies
• Hardware performance characteristics and architectural trade-offs
• Compute disaggregation and resource orchestration
• Parallelization techniques (tensor, pipeline, expert, and data parallelism)
• System architecture and rack-scale design
• Optical interconnects and next-generation data center infrastructure
• Key-Value (KV) cache management and optimization
• AI inference server architectures
• Silicon architectures and accelerator technologies
• Strong knowledge of AI inference architectures, including:
• Prefill vs. Decode design trade-offs (from silicon to business model)
• Strong understanding of AI software stacks, including AI model compilers, runtime environments, and inference optimization frameworks
• Business models supporting large-scale AI inference deployments
• Understanding of commercial drivers influencing enterprise AI infrastructure purchasing decisions
• Infrastructure Total Cost of Ownership (TCO)
• Cost-performance optimization
• Performance-per-dollar analysis
• Strong techno-economic understanding of AI compute platforms, including:
• Energy efficiency and utilization metrics
• Ability to translate complex technical concepts into compelling customer value propositions
• Proven ability to define and execute product strategy for complex technical products
• BS or MS degree in Computer Science, Computer Engineering, or similar field
• Experience developing product roadmaps informed by customer requirements, competitive analysis, and technology trends
• Experience managing highly technical products throughout the product lifecycle
• Highly structured and organized approach to product planning, prioritization, execution, and cross-functional coordination
• Strong systems-thinking approach with the ability to evaluate trade-offs across hardware, software, infrastructure, performance, and economics