Software Engineer, Inference

About Luma AI

Luma’s mission is to build multimodal AI to expand human imagination and capabilities.

Role & Responsibilities

• Ship new model architectures by integrating them into our inference engine
• Collaborate closely across research, engineering and infrastructure to streamline and optimize model efficiency and deployments
• Build internal tooling to measure, profile, and track the lifetime of inference jobs and workflows
• Automate, test and maintain our inference services to ensure maximum uptime and reliability
• Optimize deployment workflows to scale across thousands of machines
• Manage and optimize our inference workloads across different clusters & hardware providers
• Build sophisticated scheduling systems to optimally leverage our expensive GPU resources while meeting internal SLOs
• Build and maintain CI/CD pipelines for processing/optimizing model checkpoints, platform components, and SDKs for internal teams to integrate into our products/internal tooling

Background

• Strong Python and system architecture skills
• Experience with model deployment using PyTorch, Huggingface, vLLM, SGLang, tensorRT-LLM, or similar
• Experience with queues, scheduling, traffic-control, fleet management at scale
• Experience with Linux, Docker, and Kubernetes
• Bonus points: 
  • Experience with modern networking stacks, including RDMA (RoCE, Infiniband, NVLink)
  • Experience with high performance large scale ML systems (>100 GPUs)
  • Experience with FFmpeg and multimedia processing

Example Projects

• Create a resilient artifact store that manages all checkpoints across multiple versions of multiple models
• Enable hotswapping of models for our GPU workers based on live traffic patterns
• Build a robust queueing system for our jobs that take into account cluster availability and user priority
• Architect a e2e model serving deployment pipeline for a custom vendor
• Integrate our inference stack into an online reinforcement learning pipeline
• Regression & precision testing across different hardware platforms
• Building a full tracing system to trace the end-to-end lifetime of any inference workload

Tech stack

Must have

• Python
• Redis
• S3-compatible Storage
• Model serving (one of: PyTorch, vLLM, SGLang, Huggingface)
• Understanding of large-scale orchestration, deployment, scheduling (via Kubernetes or similar)

Nice to have

• CUDA
• FFmpeg

Compensation

About Luma