About Tensordyne
Tensordyne is building a new class of AI inference system designed for high-performance, power-efficient deployment of the world’s most demanding generative AI workloads.
Our platform combines purpose-built silicon, new AI math, optimized scale-up networking, and memory architecture into a tightly integrated system purpose built for large-scale AI inference. We work with hyperscalers, neoclouds, frontier model developers, enterprises, and infrastructure partners operating at the leading edge of AI.
As Tensordyne moves from system development into silicon bring-up, customer validation, beta deployments, and production rollout, we are building the technical customer organization that will sit directly between our engineering teams and the companies deploying the platform.
We are looking for an exceptional technical leader to help build and lead that function.
The Role
Tensordyne is hiring a Director of AI Systems Solutions Engineering to own and grow our most important technical customer engagements.
This is a senior, highly technical role for someone who understands modern AI infrastructure from model architecture through accelerator hardware, distributed inference, serving software, and datacenter deployment — and who can credibly engage with the engineers and architects building the next generation of AI platforms.
You will work directly with frontier model builders, hyperscalers, neoclouds, developers, infrastructure partners, and strategic customers as they evaluate and deploy Tensordyne systems.
You will also build and lead a small team of exceptional Sales and Solutions Engineers responsible for customer benchmarking, technical evaluation, NPI, model enablement, AI DC architecture, and production deployment.
This is not a traditional pre-sales engineering role. The team will operate at the frontier of a rapidly changing technology landscape, working with constantly evolving new models and requirements. The right person will be equally comfortable in a customer architecture review, helping prioritize product capabilities and roadmaps, and leading a technical evaluation with hyperscalers and frontier AI companies.
What You Will Own
- Strategic technical customer engagements: Own the technical relationship with key customers and partners from initial architecture discussions through benchmarking, evaluation, integration, deployment, and expansion.
- Technical evaluation strategy: Define how Tensordyne demonstrates system performance across KPI's like throughput, tokens/sec/user, ttft, memory utilization, power efficiency, system density, model accuracy/quality, and other relevant inference metrics.
- AI workload and model architecture engagement: Work with customers and model developers to understand current and emerging HW and model architectures, serving requirements, context lengths, parallelism strategies, model topology, quantization approaches, and inference optimization requirements.
- Benchmarking and competitive analysis: Maintain a technically rigorous understanding of Tensordyne performance relative to leading GPU and AI accelerator platforms. Ensure customer-facing comparisons are credible, reproducible, current, and aligned with real deployment requirements.
- Model enablement and optimization: Partner with compiler, runtime, kernel, systems, and SDK teams to bring important customer models and workloads onto the Tensordyne platform and identify opportunities for performance improvement.
- Forward deployment: Lead technical PoCs, remote evaluations, on-premises beta deployments, integration programs, and production readiness efforts with strategic customers.
- Customer-to-product feedback loop: Translate recurring customer requirements into clear priorities for the SDK, compiler, runtime, inference server, model support, orchestration, networking, observability, and system architecture teams.
- Technical market intelligence: Stay deeply current on model architectures, inference techniques, accelerator roadmaps, serving frameworks, competitive systems, benchmarking methodologies, and changes in the AI infrastructure market.
- Team leadership: Help recruit, develop, and lead a small team of elite Sales/Solutions Engineers capable of independently managing sophisticated technical engagements with the world’s most demanding AI infrastructure customers.
- Scalable technical GTM: Turn early customer engagements into repeatable benchmarks, evaluation frameworks, reference architectures, deployment playbooks, documentation, demos, and technical collateral that can support a rapidly growing customer base.
What We Are Looking For
We are looking for a proven AI systems leader with substantial technical depth and strong judgment.
You should bring:
- Deep understanding of modern AI inference systems, including LLM and multimodal architectures.
- Strong knowledge of AI accelerator and system architecture, including compute, memory hierarchy, interconnect, parallelism, and distributed inference.
- Experience reasoning about inference performance across latency, throughput, memory bandwidth, utilization, batching, context length, prefill, decode, and system scaling.
- Hands-on familiarity with modern AI frameworks and serving environments such as PyTorch, vLLM, SGLang, Triton, or comparable systems.
- Experience working across the boundary between AI software and accelerator hardware, ideally including GPUs, custom silicon, or emerging AI accelerators.
- Experience benchmarking and optimizing workloads on large-scale AI infrastructure.
- Strong understanding of production inference techniques including quantization, tensor/model/expert parallelism, disaggregated serving, KV-cache management, distributed execution, and related optimization strategies.
- Demonstrated ability to engage technically sophisticated external organizations including Sr technical leaders at hyperscalers, cloud providers, model developers, AI infrastructure companies, or large enterprise engineering teams.
- Experience leading high-performing Solutions Engineering, Field Engineering, Forward Deployed Engineering, or comparable technical customer teams.
- Ability to operate effectively in a fast-moving environment where the product, software stack, competitive landscape, and customer requirements are evolving simultaneously.
Particularly Relevant Experience
Candidates may come from organizations building or deploying:
- GPU or custom AI accelerator platforms
- Large-scale AI inference infrastructure
- Frontier or foundation models
- Hyperscale cloud infrastructure
- AI serving and orchestration platforms
- Compiler, runtime, or distributed AI systems
- High-performance computing or distributed systems
Experience bringing a new accelerator architecture or AI infrastructure platform from early access through customer validation and production deployment is especially valuable.
Why This Role Matters
Tensordyne is entering the stage where our technology moves from internal development into the hands of customers.
The technical customer organization will play a central role in that transition.
This team will help determine which workloads we prioritize, how customers evaluate our platform, how quickly new models become production-ready, how effectively customer feedback reaches engineering, and ultimately how Tensordyne systems are adopted at scale.
We are looking for someone who wants to build that capability from the beginning — and establish the technical standard for how Tensordyne engages with the companies defining the future of AI infrastructure.
Tensordyne Values
- Think big. Pursue ambitious technical and business goals.
- Aim for excellence. Quality matters in everything we build and deliver.
- Own it and get it done. Take responsibility and drive results.
- Operate with integrity. Be direct, transparent, and respectful.
- Win as a team. Make the people around you more effective.
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.
Skills Required
- Deep understanding of modern AI inference systems, including LLM and multimodal architectures.
- Strong knowledge of AI accelerator and system architecture, including compute, memory hierarchy, interconnect, parallelism, and distributed inference.
- Experience reasoning about inference performance across latency, throughput, memory bandwidth, utilization, batching, context length, prefill, decode, and system scaling.
- Hands-on familiarity with modern AI frameworks and serving environments such as PyTorch, vLLM, SGLang, Triton, or comparable systems.
- Experience working across AI software and accelerator hardware, ideally including GPUs, custom silicon, or emerging AI accelerators.
- Experience benchmarking and optimizing workloads on large-scale AI infrastructure.
- Strong understanding of production inference techniques, including quantization, tensor/model/expert parallelism, disaggregated serving, KV-cache management, and distributed execution.
- Demonstrated ability to engage technically sophisticated external organizations, including hyperscalers, cloud providers, model developers, AI infrastructure companies, or large enterprise engineering teams.
- Experience leading high-performing Solutions Engineering, Field Engineering, Forward Deployed Engineering, or comparable technical customer teams.
- Ability to operate effectively in a rapidly changing environment with evolving products, software, competition, and customer requirements.
- Experience with GPU or custom AI accelerator platforms, large-scale AI inference infrastructure, frontier or foundation models, hyperscale cloud infrastructure, AI serving, orchestration, compilers, runtimes, distributed AI systems, or high-performance computing.
- Experience bringing a new accelerator architecture or AI infrastructure platform from early access through customer validation and production deployment.
What We Do
Every leap in AI has followed the same pattern: first we made models bigger, then we tailored them, and now we let them think longer. Each step (scaling law) truly adds intelligence. But in the end we land on the same runway: inference, and demand is exploding while power supply lags. We asked: what if the next step isn’t stacking another law on top, but a zeroth law beneath them all. A law that changes AI math. Because after all, AI is math, trillions of multiplies, and multiplication burns watts. Tensordyne uses logarithmic compute to turn multiplies into adds, cutting power at the root. We’ve cast our proprietary logarithmic math into custom silicon, hardware, interconnect, and system software. The result: one integrated system for multimodal GenAI inference designed for Hyperscaler and Neo Cloud data centers. What this means for our customers: With Tensordyne they can run the world’s largest multimodal models for thousands of users, with fewer racks, less power, and lower cost. We’re well-funded and fast-moving, with co-headquarters in Sunnyvale, California and Munich, Germany, and a distributed team across North America and Europe. Join us to change how the world runs Gen AI.
