Inference Systems Engineer - Kubernetes & GPU Fleet

Luma; New York (NY); On-site; USD 195,000 - 345,000; Full time

Job summary

Luma is seeking a systems engineer to own serving models at scale, integrating new architectures into the inference engine, and scaling deployments across thousands of machines. You will tackle scheduling, fleet management, deployment pipelines, and reliability across clusters and hardware providers.

This role fits a strong systems engineer comfortable with model serving, Kubernetes, and a focus on end-to-end infrastructure rather than pure modeling. Expect a hands-on, high-impact environment.

Qualifications

• Strong Python and system-architecture skills.
• Experience deploying models with PyTorch, Hugging Face, vLLM, SGLang, TensorRT-LLM, or similar.
• Experience with queues, scheduling, traffic control, and fleet management at scale.
• Experience with Linux, Docker, and Kubernetes, and with orchestration, deployment, and scheduling.
• Familiarity with Redis and S3-compatible storage.

Responsibilities

• Own shipping new model architectures by integrating them into the inference engine.
• Collaborate across research, engineering, and infrastructure to optimize model efficiency and deployments.
• Build internal tooling to measure, profile, and track the lifetime of inference jobs and workflows.
• Automate, test, and maintain inference services for maximum uptime and reliability.
• Manage and optimize inference workloads across clusters and hardware providers, and scale deployments across thousands of machines.
• Build scheduling systems that use expensive GPU resources optimally while meeting SLOs, and maintain CI/CD for model checkpoints and SDKs.

Skills

Python

System architecture

Model deployment

Linux

Docker

Job description

Luma is seeking a systems engineer to own serving models at scale, integrating new architectures into the inference engine, and scaling deployments across thousands of machines. You will tackle scheduling, fleet management, deployment pipelines, and reliability across clusters and hardware providers.

This role fits a strong systems engineer comfortable with model serving, Kubernetes, and a focus on end-to-end infrastructure rather than pure modeling. Expect a hands-on, high-impact environment.