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.