Research Scientist / Engineer – Training Infrastructure

Luma AI; San Francisco (CA); Hybrid; USD 187,500 - 395,000; Full time

Job summary

Luma AI in SF Bay Area is hiring for a Research Scientist/Engineer in Training Infrastructure. You will design distributed training systems for thousands of GPUs and enable researchers to push multimodal foundation models.

You should have deep experience with PyTorch, CUDA and distributed systems, plus Linux and scripting. The role offers remote/international options including London; hybrid/remote work supported.

Qualifications

• Experience with distributed PyTorch training on GPUs.

Responsibilities

• Design, implement, and optimize distributed training systems for models with thousands of GPUs
• Research and implement advanced parallelization techniques (FSDP, Tensor Parallel, Pipeline Parallel, Expert Parallel)
• Build monitoring, visualization, and debugging tools for large-scale training runs
• Optimize training stability, convergence, and resource utilization across massive clusters

Skills

Large-scale GPU training

Distributed PyTorch training

GPU clusters

Linux scripting

Containerization

Cloud infrastructure

NCCL

MPI

CUDA

Tools

PyTorch

CUDA

Distributed systems

NCCL

MPI

Linux

Scripting

Containerization

Cloud infra

Job description

View all jobs Research Scientist / Engineer – Training Infrastructure SF Bay Area, CA • Remote, International • London, UK Systems Research & Engineering Remote • Hybrid Full-time

About Luma AI

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. So, 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 effect change.

About the Role

The Training Infrastructure team at Luma is responsible for building and maintaining the distributed systems that enable training of our large-scale multimodal models across thousands of GPUs. This team ensures our researchers can focus on innovation while having access to reliable, efficient, and scalable training infrastructure that pushes the boundaries of what's possible in AI model development. We are looking for engineers with significant experience solving hard problems in PyTorch, CUDA and distributed systems. You will work alongside the rest of the research team to build & train cutting edge foundation models on thousands of GPUs that are built to scale from the ground up.

Responsibilities

• Design, implement, and optimize efficient distributed training systems for models with thousands of GPUs
• Research and implement advanced parallelization techniques (FSDP, Tensor Parallel, Pipeline Parallel, Expert Parallel)
• Build monitoring, visualization, and debugging tools for large-scale training runs
• Optimize training stability, convergence, and resource utilization across massive clusters

Experience

• Extensive experience with distributed PyTorch training and parallelisms in foundation model training
• Deep understanding of GPU clusters, networking, and storage systems
• Familiarity with communication libraries (NCCL, MPI) and distributed system optimization
• (Preferred) Strong Linux systems administration and scripting capabilities
• (Preferred) Experience managing training runs across >100 GPUs
• (Preferred) Experience with containerization, orchestration, and cloud infrastructure

Compensation

The base pay range for this role is $187,500 – $395,000 per year.