Senior Systems Engineer - Large-Scale AI Inference

Speedrun Talent Network; Greater London; Hybrid; GBP 90,000 - 140,000; Full time

Job summary

Luma is hiring a systems engineer to own large-scale model serving, integrating new architectures into the inference engine and scaling deployments across thousands of machines. You will optimize scheduling, fleet management, and reliability across clusters and hardware providers.

Ideal candidates have strong Python and system-architecture skills, experience with PyTorch, Hugging Face, and Kubernetes, and a track record of building tooling for profiling and uptime.

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

• Ship 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

PyTorch

Hugging Face

vLLM

SGLang

TensorRT-LLM

Queues & scheduling

Linux

Tools

Docker

Kubernetes

Redis

S3-compatible storage

CUDA

Job description

Luma is hiring a systems engineer to own large-scale model serving, integrating new architectures into the inference engine and scaling deployments across thousands of machines. You will optimize scheduling, fleet management, and reliability across clusters and hardware providers.

Ideal candidates have strong Python and system-architecture skills, experience with PyTorch, Hugging Face, and Kubernetes, and a track record of building tooling for profiling and uptime.