Luma fertility blends global science with human care to offer advanced fertility treatments, zero wait times, expert doctors, and transparent outcomes. Backed by Peak XV and a16z.
About the role
You'll build the distributed systems that train Luma's large-scale multimodal models across thousands of GPUs, so researchers can focus on innovation on top of reliable, efficient, scalable infrastructure. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this role
You'll build the distributed systems that train Luma's large-scale multimodal models across thousands of GPUs, so researchers can focus on innovation on top of reliable, efficient, scalable infrastructure.
This is hard PyTorch, CUDA, and distributed-systems work — advanced parallelism, training stability, and utilization across massive clusters. It fits an engineer who's solved real problems training foundation models at scale. If you haven't worked at the level of FSDP and multi-node training, this is the wrong depth.
Design, implement, and optimize efficient distributed training systems for models across thousands of GPUs.
Research and implement advanced parallelization (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.
One way the first 90 could unfold.
Days 1–30 — Immerse & Diagnose: Learn the current training stack and where stability and utilization hurt at scale.
Days 30–60 — Ship & Validate: Land a parallelization or stability improvement that measurably helps a real training run.
Days...
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