lumalabs-ai18mo ago
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Research Scientist / Engineer – Training Infrastructure
Data ScientistData
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Quick Summary
Overview
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.
Technical Tools
Data ScientistData
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
~1 min readThe 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
~1 min read- →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
- 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
What We Offer
~1 min readThe base pay range for this role is $187,500 – $395,000 per year.
Location & Eligibility
Where is the job
Sf Bay Area, United States
Hybrid — some on-site time required
Who can apply
US
Listing Details
- Posted
- March 12, 2025
- First seen
- September 26, 2026
- Last seen
- September 28, 2026
Posting Health
- Days active
- 1
- Repost count
- 0
- Trust Level
- 21%
- Scored at
- September 28, 2026
Signal breakdown
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