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Principal Product Manager, Augmented Memory Grid (AMG)

10 hours, 37 minutes ago
United States
Lead
Product Manager
Product Management
Kubernetes Machine Learning
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WEKA

WEKA

WEKA provides a cutting-edge data platform for cloud and on-premises data management, offering WekaFS for high-performance storage solutions in data-intensive environments.

IT Services
201-500
Founded 2014
$294M raised
View All Jobs 24

Description

  • Own the AMG product roadmap, including KV-cache and prefix-cache offload, memory tiering, and inference-serving integrations.
  • Partner with engineering to define architecture trade-offs across GPU memory, high-performance networking, and distributed storage.
  • Translate inference performance bottlenecks such as time-to-first-token, throughput, and context length into product requirements.
  • Gather requirements, validate benchmarks, and prioritize features with enterprise customers and GPU cloud partners running production inference workloads.
  • Partner with NVIDIA and other silicon and inference-stack providers on joint roadmaps and certification efforts.
  • Define and track benchmarks, including time-to-first-token, throughput, and cache-hit rate, to demonstrate AMG value.
  • Support sales and field teams with technical positioning, competitive differentiation, and enterprise deal support.
  • Present product strategy and roadmaps to internal stakeholders and external customers.

Requirements

  • 10+ years of product management experience, ideally including infrastructure, ML platforms, or developer-facing technical products.
  • Hands-on product or engineering experience with LLM inference serving, such as vLLM, NVIDIA Triton, TensorRT-LLM, NIM, Ray Serve, or comparable platforms.
  • Fluency in KV-cache, prefix/context caching, quantization, and batching strategies.
  • Working knowledge of production LLM serving, including context windows, multi-tenant serving, and GPU scheduling.
  • Knowledge of high-performance networking and distributed-systems technologies such as RDMA, GPUDirect Storage, and NVMe-oF.
  • Experience translating model-level constraints and inference performance needs into infrastructure requirements.
  • Track record working directly with large enterprise customers on requirements gathering, production deployments, and SLAs.
  • Strong cross-functional leadership, strategic planning, and ability to influence without authority.
  • Ability to communicate complex technical concepts to technical and non-technical stakeholders.
  • Experience at a GPU cloud, inference platform, or AI infrastructure startup; familiarity with AI/ML storage systems; and experience partnering with NVIDIA or other accelerator vendors (preferred).

Interested in this position?

Apply directly on the company website

Apply Now

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