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Inferact

Open-source LLM inference engine with scalability

Member of Technical Staff - Inference

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeSingaporeIn-person

About the Job

Requirements

  • A bachelor's degree or equivalent experience in computer science, engineering, or a similar field.
  • A deep understanding of transformer architectures and their variants.
  • Strong programming skills in Python and experience with PyTorch internals.
  • Experience with large language model inference systems such as vLLM, TensorRT-LLM, SGLang, or TGI.
  • Ability to read and implement model architectures and inference techniques from research papers.
  • Ability to contribute performant, maintainable code and debug in complex machine learning codebases.

Responsibilities

  • Work on inference runtime engineering for large language model and diffusion model serving.
  • Optimize how models execute across diverse hardware and architectures.
  • Contribute to the core vLLM inference engine and advance inference performance.

Desired Qualifications

  • A deep understanding of key-value cache memory management, prefix caching, and hybrid model serving.
  • Familiarity with reinforcement learning frameworks and algorithms for large language models.
  • Experience with multimodal inference across audio, image, video, and text.
  • Contributions to open-source machine learning or systems infrastructure projects.
  • Implementation of core features in vLLM or other inference engine projects.
  • Contributions to vLLM integrations such as verl, OpenRLHF, Unsloth, or LlamaFactory.
  • Authorship of widely shared technical blogs or side projects on vLLM or large language model inference.

About the Company

Inferact

Open-source LLM inference engine with scalability

Inferact builds AI inference infrastructure centered on vLLM, an open-source large language model inference engine. It offers a managed inference service for enterprises with serverless deployment, observability, troubleshooting, disaster recovery, and multi-tenant Kubernetes scaling. vLLM uses PagedAttention to optimize GPU memory, lowering costs and latency while preserving model quality, and Inferact supports new architectures and hardware under vendor-neutral governance with PyTorch Foundation. The goal is to commoditize AI inference and deliver a production-ready operating layer for the AI stack.

Company size
11-50
Company stage
Seed
Total funding
$150M
Headquarters
San Francisco, CA
Founded
2025

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