Automatically optimizes GPU? utilization and parallelism strategies across multi-GPU?/TPU clusters for distributed model training and inference.
Added May 23, 2026
Medium opportunity (67%)
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ML? teams building large-scale models struggle to efficiently coordinate distributed training across GPU?/TPU clusters, with poor GPU? utilization and complex tuning of data, model, and pipeline parallelism strategies. Engineers spend significant time hand-tuning communication patterns, batching, and hardware co-design instead of focusing on model research.
A platform that profiles distributed ML? workloads and automatically configures optimal parallelism strategies (data, model, pipeline) across GPU?/TPU clusters. It monitors GPU? utilization in real-time, recommends communication optimizations, and co-designs batching and GPU?-aware data loading with training and inference pipelines.
The explosion of multimodal and large foundation model training across companies like xAI, Anthropic, ByteDance, and Physical Intelligence has made distributed training optimization a critical bottleneck, with GPU? compute costs making even small efficiency gains worth millions.
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We run nearly 30 models across heterogeneous hardware, and keeping that fleet fast, reliable, and cost-effective is a serious engineering challenge. You'll build the GPU management and scheduling platform that sits at the center of it — collecting utilization and load metrics, interpreting what they actually mean, and using them to make real-time decisions about admission control and scaling. The goal: route and schedule inference calls so we use our capacity efficiently without exceeding it, an
pAGI Infra team builds and operates the systems that make large-scale model training and evaluation reliable, efficient, and easy to run. Our work spans distributed training infrastructure, inference and grading platforms, compute scheduling, and research tooling. We partner closely with researchers and engineering teams to turn new research needs into dependable infrastructure, improve GPU efficiency, and shorten the path from an experiment to a validated model.
Explore Al/ML infrastructure acceleration, leveraging DPUs, GPUs, and custom hardware to optimize distributed training and inference. Drive end-to-end performance optimization, from OS kernels and drivers to user-space runtime systems.
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