Research Scientist / Engineer – Training Infrastructure
Builds and optimizes distributed training infrastructure for large-scale multimodal AI models across thousands of GPUs. Requires deep expertise in PyTorch, CUDA, parallelization techniques, and GPU clusters.
About the job
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
- Experience managing training runs across >100 GPUs
- Experience with containerization, orchestration, and cloud infrastructure
Compensation
Base pay range: $187,500 – $395,000 per year
Skills
PyTorch, CUDA, Distributed Systems, Fsdp, Tensor Parallel, Pipeline Parallel, Expert Parallel, Nccl, Mpi, Kubernetes
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