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WekaIO

AI-native data platform for high-performance workloads

Tech Lead – AI Inference

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeRemote in USANo salary listed

About the Job

Requirements

  • At least 8 years of professional software engineering experience, with proven experience leading engineers and owning complex production systems, ideally in AI/ML infrastructure or high-performance computing.
  • Hands-on expertise with LLM serving systems, including KV cache reuse, disaggregated prefill/decode, continuous batching, and multi-tier GPU memory hierarchies (HBM to NVMe), and familiarity with vLLM, LMCache, NIXL/NVIDIA Dynamo, or similar frameworks.
  • Strong Python and C++ skills; Rust is a plus. Solid understanding of CUDA, GPU memory management, and high-performance I/O, including GPUDirect Storage (GDS), RDMA, and NVMe data paths.
  • Experience deploying and scaling GPU workloads on Kubernetes, with familiarity in RDMA networking, bare-metal GPU clusters (H100/A100), and high-throughput distributed storage.
  • Ability to mentor and develop engineers, run effective 1:1s, support career growth, and balance technical execution with long-term team health.
  • A strong engineering craftsmanship record of building reliable, high-throughput systems and continuously improving engineering practices.

Responsibilities

  • Lead and grow a squad of three developers, balancing hands-on technical contribution with people leadership, setting direction, unblocking the team, and driving execution on high-performance systems that optimize LLM serving.
  • Take end-to-end ownership of AMG's core inference infrastructure, from the NVMe Token Warehouse and GDS data paths to the vLLM/LMCache serving stack, and drive technical decisions and delivery outcomes.
  • Guide engineers through the design, implementation, and delivery of high-throughput, low-latency LLM inference systems, setting standards for code quality, architecture, and reliability.
  • Contribute hands-on across the AMG stack, including Python, C++, CUDA, vLLM, NIXL/Dynamo, and Kubernetes, while providing technical leadership to the team.
  • Address inference engineering challenges including disaggregated prefill/decode, persistent off-HBM KV caching, RDMA-based transport, and multi-tier GPU memory hierarchies.
  • Mentor and coach engineers through regular 1:1s, career coaching, and sprint reviews, and foster ownership, collaboration, and technical excellence within the AMG team.
  • Track the evolving inference ecosystem, benchmark tools such as SGLang, TRT-LLM, and NVIDIA Dynamo, and help the team decide when to adopt, build, or pivot.

Desired Qualifications

  • Experience in AI/ML infrastructure or high-performance computing is ideal.
  • Rust is a plus.
  • Familiarity with vLLM, LMCache, NIXL/NVIDIA Dynamo, or similar LLM serving frameworks.
  • Familiarity with RDMA networking, bare-metal GPU clusters (H100/A100), and high-throughput distributed storage.

About the Company

WekaIO

AI-native data platform for high-performance workloads

WEKA is an AI-native data platform that provides a cloud- and hardware-agnostic software solution for performance-heavy AI, ML, and GPU workloads. The WEKA Data Platform organizes and speeds data across on-premises, cloud, edge, and hybrid environments to turn isolated data into dynamic pipelines. It delivers high throughput and scalable data management to support enterprise AI initiatives and research workloads, helping teams gain faster insights. WEKA differentiates itself with a single scalable platform that works across multiple infrastructure footprints and emphasizes energy efficiency, aiming to simplify and accelerate data-driven AI workflows for large enterprises and research groups while replacing legacy data silos.

Company size
501-1,000
Company stage
Series E
Total funding
$465.1M
Headquarters
Campbell, CA
Founded
2013

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