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Inferact

Open-source LLM inference engine with scalability

Forward Deployed Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • A bachelor's degree or equivalent experience in computer science, engineering, systems, machine learning, or a similar area.
  • Strong software engineering ability in Python, Go, TypeScript, or similar, with experience building production-quality integrations, tooling, services, automation, or prototypes.
  • Hands-on experience deploying or operating machine-learning systems, model serving, AI infrastructure, cloud platforms, Kubernetes, or high-scale backend systems in production.
  • Ability to work directly with sophisticated customer engineering teams, understand ambiguous technical environments, and personally drive implementations and debugging to resolution.
  • Strong systems debugging skills across application, runtime, infrastructure, networking, identity, storage, observability, and distributed-system boundaries.
  • Ability to reason about latency, throughput, batching, model/runtime compatibility, scaling, reliability, and cost tradeoffs in production inference environments.
  • High ownership and strong technical communication, with the judgment to distinguish one-off customer work from problems that should become reusable product capabilities.

Responsibilities

  • Deploy, integrate, debug, and optimize vLLM-powered inference systems across cloud, Kubernetes, GPU, networking, and model-serving environments for customers.
  • Move from architecture discussions to implementation, own difficult production problems end-to-end, and work closely with core product and engineering teams to turn field learnings into reusable product capabilities.

Desired Qualifications

  • Experience with vLLM, SGLang, TensorRT-LLM, TGI, Ray Serve, BentoML, or other LLM inference and model-serving systems.
  • Experience with NVIDIA or AMD GPUs, CUDA or ROCm, GPU scheduling, multi-GPU serving, or accelerator-backed infrastructure.
  • Experience deploying infrastructure software into enterprise, regulated, security-sensitive, or bring-your-own-cloud environments.
  • Experience building APIs, SDKs, CLIs, developer tooling, deployment platforms, control planes, or infrastructure products used by technical teams.
  • Experience profiling latency, throughput, concurrency, GPU utilization, bottlenecks, and performance regressions.
  • Contributed to open-source machine-learning systems, inference infrastructure, cloud infrastructure, Kubernetes, or developer tooling.
  • Worked in a forward-deployed, customer engineering, field engineering, or highly technical solutions role involving personally writing and shipping code.
  • Built deployment playbooks, reference architectures, automation, or tooling that materially reduced customer time-to-production.
  • Resolved severe customer-facing production issues that crossed multiple technical layers and required close partnership with core engineering.
  • Turned repeated customer problems into reusable product features, abstractions, documentation, or platform improvements.

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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