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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and optimizing distributed training systems for large-scale models, with a strong focus on GPU utilization and parallelization techniques. Proficient in building monitoring tools and ensuring the reliability of training runs across extensive GPU clusters.
Highest-signal resume keywords
Distributed PyTorch TrainingGPU Cluster ManagementParallelization TechniquesLinux Systems AdministrationContainerization and Orchestration
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Distributed Training SystemsParallelizationGPU OptimizationScriptingMonitoring Tools
Tools & Technologies
NCCLMPICloud Infrastructure
Industry Keywords
Foundation-Model TrainingLarge-Scale TrainingResource UtilizationNetworkingStorage Systems
Tech Stack
Tools & technologiesCloudDistributed SystemsLinuxPyTorch
About the role
Key responsibilities & impact- Design, implement, and optimize efficient distributed training systems for models across thousands of GPUs
- Research and implement advanced parallelization, including FSDP, Tensor Parallel, Pipeline Parallel, and Expert Parallel
- Build monitoring, visualization, and debugging tools for large-scale training runs
- Optimize training stability, convergence, and resource utilization across massive clusters
- Learn the current training stack and diagnose stability and utilization issues at scale during the first 30 days
- Ship and validate a parallelization or stability improvement that measurably helps a real training run during days 30–60
- Build monitoring and tooling to keep large runs reliable and efficient during days 60–90
- Build distributed systems that train Luma's large-scale multimodal models across thousands of GPUs
- Enable researchers to focus on innovation on top of reliable, efficient, scalable infrastructure
Requirements
What you’ll need- Extensive 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
- Strong Linux systems administration and scripting
- Experience managing training runs across 100+ GPUs
- Experience with containerization, orchestration, and cloud infrastructure
Benefits
Comp & perks- Equal opportunity employer
- Optional diversity and inclusion survey; participation is voluntary and refusal will not affect the job application