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Tensordyne

B2B data storage and analytics service

Director of AI Systems Solutions Engineering

Implementation & Technical Account ManagementConfirmed live in the last 24 hours
Full-timeSunnyvale, CA, USANo salary listedIn-person

About the Job

Requirements

  • Deep understanding of modern AI inference systems, including large language model 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, and expert parallelism, disaggregated serving, KV-cache management, distributed execution, and related optimization strategies.
  • Demonstrated ability to engage technically sophisticated external organizations, including senior 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.

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 key performance indicators such as throughput, tokens per second per user, time to first token, memory utilization, power efficiency, system density, model accuracy and quality, and other relevant inference metrics.
  • Work with customers and model developers to understand current and emerging hardware 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, ensuring customer-facing comparisons are credible, reproducible, current, and aligned with real deployment requirements.
  • Partner with compiler, runtime, kernel, systems, and software development kit teams to bring important customer models and workloads onto the Tensordyne platform and identify opportunities for performance improvement.
  • Lead technical proofs of concept, remote evaluations, on-premises beta deployments, integration programs, and production readiness efforts with strategic customers.
  • Translate recurring customer requirements into clear priorities for the software development kit, compiler, runtime, inference server, model support, orchestration, networking, observability, and system architecture teams.
  • Stay 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 Sales and Solutions Engineers capable of independently managing sophisticated technical engagements with demanding AI infrastructure customers.
  • Turn early customer engagements into repeatable benchmarks, evaluation frameworks, reference architectures, deployment playbooks, documentation, demonstrations, and technical collateral to support a growing customer base.

Desired Qualifications

  • Experience 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, or 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.

About the Company

Tensordyne

B2B data storage and analytics service

Tensordyne provides B2B data storage and data access services for electronic communications networks. Its platform stores and retrieves data for clients, including user preferences used to personalize experiences, while keeping privacy at the forefront. It also analyzes stored data to generate anonymous statistical insights that help clients make data-driven decisions. Revenue likely comes from charging clients for storage, access, and analytics services. Compared with competitors, Tensordyne emphasizes privacy and data protection, offering analytics strictly on anonymized data and focusing on essential storage and transmission needs for businesses. The company’s goal is to help organizations run reliable, secure data storage and transmission while enabling privacy-safe, data-driven business intelligence.

Company size
51-200
Company stage
Series C
Total funding
$175.9M
Headquarters
Sunnyvale, CA
Founded
2017

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