Role Overview
Luma AI is hiring a mid-level Research Scientist / Engineer – Training Infrastructure. This is a full-time role in CA. posted yesterday. Full responsibilities, required qualifications, and the apply link are listed in the description below.
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Job Description
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.
About Luma
Luma’s mission is to build unified general intelligence that can generate, understand, and operate in the physical world.
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.
Frequently Asked Questions
How do I apply for the Research Scientist / Engineer – Training Infrastructure position at Luma AI?
Use the Apply button above to submit your application directly to Luma AI. Most applications take less than 5 minutes if your resume and contact details are ready, and you'll be routed to the employer's official application system to finish.
Where is the Research Scientist / Engineer – Training Infrastructure position at Luma AI located?
This position is based in CA. Luma AI has not indicated remote or hybrid options for this role, so candidates should plan for on-site work.
What does a Research Scientist / Engineer – Training Infrastructure at Luma AI earn?
Luma AI has not disclosed a salary range in this posting. Many employers share specifics later in the interview process; you can also ask during a recruiter screen if compensation transparency is important to you.
When was the Research Scientist / Engineer – Training Infrastructure role at Luma AI posted?
This role was posted on June 13, 2026 (yesterday). It's still listed as actively hiring; we re-confirm openings against the source system multiple times per day and remove closed roles.
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