Qualification

Ability to reason about latencyExperience with vLLMExperience with NVIDIA or AMD GPUsExperience building APIsExperience profiling latencyContributed to open-source ML systemsWorked in a forward-deployedBuilt deployment playbooks

Required

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, with 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, 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.

Preferred

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.

Member of Technical Staff, Forward Deployed Engineer at Inferact · LumiJobs

Member of Technical Staff, Forward Deployed Engineer

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Inferact

More roles at Inferact