Job description

About Tensordyne: 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 Occasional external spokesperson (conferences etc.) 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 AI inference server architectures 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 optimization frameworks. Demonstrated ability to connect low-level hardware capabilities with system-level performance and customer business outcomes. Business & Commercial Acumen Strong techno-economic understanding of AI compute platforms, including: Cost-performance optimization Infrastructure Total Cost of Ownership (TCO) Performance-per-dollar analysis Energy efficiency and utilization metrics Business models supporting large-scale AI inference deployments Ability to translate complex technical concepts into compelling customer value propositions. Understanding of commercial drivers influencing enterprise AI infrastructure purchasing decisions. Product Management & Strategic Skills Proven ability to define and execute product strategy for complex technical products. Experience developing product roadmaps informed by customer requirements, competitive analysis, and technology trends. Strong systems-thinking approach with the ability to evaluate trade-offs across hardware, software, infrastructure, performance, and economics. Highly structured and organized approach to product planning, prioritization, execution, and cross-functional coordination. Experience managing highly technical products throughout the product lifecycle. BS or MS degree in Computer Science, Computer Engineering, or similar field. Tensordyne's culture was built on the following values Put people first. We only succeed when our people succeed. Ethics and integrity always; Being open, honest, and respectful of everyone. Think Big. Be ambitious and have audacious goals of global scale. Aim for excellence. Quality and excellence count in everything we do. Own it and get it done. Results matter! Make each person better together, than they would be as an individual. Embrace each others’ differences, and embrace that there will be differences. Tensordyne is an equal opportunity employer. We believe that a diverse team is better at tackling complex problems and coming up with innovative solutions. All qualified applicants will receive consideration for employment without regard to age, color, gender identity or expression, marital status, national origin, disability, protected veteran status, race, religion, pregnancy, sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. A note to Recruitment Agencies: Please don’t reach out to Tensordyne employees or leaders about our roles -- we’ve got it covered. We don’t accept unsolicited agency resumes and we are not responsible for any fees related to unsolicited resumes. Thank you for your understanding.

Requirements

Must-haves

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
Occasional external spokesperson (conferences etc.)
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
AI inference server architectures
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 optimization frameworks.
Demonstrated ability to connect low-level hardware capabilities with system-level performance and customer business outcomes.
Business & Commercial Acumen

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

Platform: greenhouse
Effort: quick