Director, AI Systems Solutions Engineering

Tensordyne•Sunnyvale, CA

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

Own the technical relationship with key customers and partners from initial architecture discussions through benchmarking, evaluation, integration, deployment, and expansion.
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.
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.
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.
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.
Lead technical PoCs, remote evaluations, on-premises beta deployments, integration programs, and production readiness efforts with strategic customers.
Translate recurring customer requirements into clear priorities for the SDK, compiler, runtime, inference server, model support, orchestration, networking, observability, and system architecture teams.
Stay deeply current on model architectures, inference techniques, accelerator roadmaps, serving frameworks, competitive systems, benchmarking methodologies, and changes in the AI infrastructure market.
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.
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.