Director of Product Management

Artificial intelligence (AI) is transforming our world. It can perform cognitive functions that previously only humans could do, such as perceiving interactions across different modalities and environments - with the ability to quickly learn and then solve complex problems. Tensordyne is an AI system solution company that builds very high-performance, low-power generative AI inference systems. Our mission, through the creation of custom silicon, hardware and software, is to enable multimodal Generative AI inference acceleration at scale, with safe, sustainable, high-performance systems for our hyperscaler and neocloud data center customers.

We are at the leading edge of advancing the latest research and product improvements for generative Al inference solutions that will make Al even more advantageous for compelling new generative AI applications. Tensordyne is a well funded, fast-paced startup company with headquarters in both Sunnyvale, CA, and Munich, Germany. We also have many talented team members working remotely across North America and Europe.

We take care of our people and their families with comprehensive benefits, competitive compensation, flexible spending options, and recognition programs, because building category-defining technology starts with a healthy, supported team. Come join us as we shape the future of multimodal generative artificial intelligence!

About the role:

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). We need a self-starter reporting to the VP of product management, you will help focus AI compute product efforts that will define the future of inference at customers of Tensordyne.

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!

Where you'd help us:

• Leading the definition of Tensordyne's next generation of Al inference compute products, with an innovative yet pragmatic and well-communicated technology roadmap in, shaping the company's GTM strategy
• Engaging with our external technology partners, customers, Al industry researchers and internal engineering & business stakeholders to coalesce insights and requirements into winning Al product plans and strategies.
• Researching, defining and driving essential competitive analysis, key metrics tracking, cost modeling, customer demos, and product release schedules to ensure Tensordyne's superiority to alternative solutions.
• Overseeing and ensuring the success of key customers' projects, anticipating their software and hardware needs, while reacting as necessary to pertinent feedback.
• Working hand-in-hand with marketing to design marketing campaigns with the goal of promoting Tensordyne's Al technology, products and demonstrating thought leadership

Qualifications:

Technical

(

NOTE:

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)

• Deep understanding of the execution flow of modern AI inference systems, particularly large-scale Mixture-of-Experts (MoE) models, and world models
• Strong knowledge of AI inference architectures, including:
• Key-Value (KV) cache management and optimization
• Multi-user and multi-agent inference scheduling
• Parallelization techniques (tensor, pipeline, expert, and data parallelism)
• Compute disaggregation and resource orchestration
• Modern AI inference system KPIs, performance metrics, Pareto optimization, and efficiency trade-offs.
• Prefill vs. Decode design trade-offs (from silicon to business model)
• Comprehensive understanding of AI compute infrastructure, including:
• Silicon architectures and accelerator technologies
• Hardware performance characteristics and architectural trade-offs
• System architecture and rack-scale design
• High-performance networking topologies
• Optical interconnects and next-generation data center infrastructure
• Strong understanding of AI software stacks, including AI model compilers, runtime environments, and inference…