Member of Technical Staff, Forward Deployed Engineer

The role

You will deploy, integrate, debug, and optimize vLLM-powered inference systems in customer environments. You will work across cloud, Kubernetes, GPU, networking, and model-serving environments to turn field learnings into reusable product capabilities.

What you will do

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

Required qualifications

• Bachelor's degree or equivalent experience in computer science, engineering, systems, machine learning, or similar
• Strong software engineering ability in Python, Go, TypeScript, or similar
• Experience building production-quality integrations, tooling, services, automation, or prototypes
• Hands-on experience deploying or operating ML systems, model serving, AI infrastructure, cloud platforms, Kubernetes, or high-scale backend systems in production
• Ability to work directly with sophisticated customer engineering teams
• Ability to 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

Nice to have

• 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 / 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 ML systems, inference infrastructure, cloud infrastructure, Kubernetes, or developer tooling
• Worked in a forward-deployed, customer engineering, field engineering, or highly technical solutions role where you personally wrote and shipped 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

Benefits

• Health benefits
• Dental benefits
• Vision benefits
• 401(k) company match

Before you apply

• We sponsor visas on a case-by-case basis

About the company

Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, the company sits at the intersection of models and hardware.