Luma’s mission is to build multimodal AI to expand human imagination and capabilities. We believe that multimodality is critical for intelligence. To go beyond language models and build more aware, capable and useful systems, the next step function change will come from vision. We are working on training and scaling up multimodal foundation models for systems that can see and understand, show and explain, and eventually interact with our world to affect change. We know we are not going to reach our goal with reliable & scalable infrastructure, which is going to become the differentiating factor between success and failure.

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

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
• FFmpeg