Director of AI Systems Solutions Engineering

Join Tensordyne as the Director of AI Systems Solutions Engineering, a senior technical role focused on driving key customer engagements and shaping the future of AI infrastructure. You will work directly with leading model builders, hyperscalers, and strategic customers, while building and leading a team of exceptional Sales and Solutions Engineers. This role requires a deep understanding of AI infrastructure, model architecture, and production deployment.

- Establish and maintain technical relationships with key customers and partners, guiding them through architecture discussions, benchmarking, evaluation, integration, deployment, and expansion. - Define and implement strategies for demonstrating system performance across key performance indicators (KPIs) and ensure customer-facing comparisons are credible and aligned with real deployment requirements. - Lead technical proof of concepts (PoCs), remote evaluations, on-premises beta deployments, integration programs, and production readiness efforts with strategic customers.

- We are looking for a proven AI systems leader with substantial technical depth and strong judgment - Strong knowledge of AI accelerator and system architecture, including compute, memory hierarchy, interconnect, parallelism, and distributed inference - Deep understanding of modern AI inference systems, including LLM and multimodal architectures - 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 - Ability to operate effectively in a fast-moving environment where the product, software stack, competitive landscape, and customer requirements are evolving simultaneously - Experience benchmarking and optimizing workloads on large-scale AI infrastructure - Experience reasoning about inference performance across latency, throughput, memory bandwidth, utilization, batching, context length, prefill, decode, and system scaling - Strong understanding of production inference techniques including quantization, tensor/model/expert parallelism, disaggregated serving, KV-cache management, distributed execution, and related optimization strategies - 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 leading high-performing Solutions Engineering, Field Engineering, Forward Deployed Engineering, or comparable technical customer teams - Large-scale AI inference infrastructure - 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 - GPU or custom AI accelerator platforms - Frontier or foundation models - Candidates may come from organizations building or deploying: - Hyperscale cloud infrastructure - AI serving and orchestration platforms - Compiler, runtime, or distributed AI systems