133 Signals

GPU Cluster Optimization Platform for Distributed ML Training

Automatically optimizes GPU utilization and parallelism strategies across multi-GPU/TPU clusters for distributed model training and inference.

Added May 23, 2026

ML Infrastructure

Developer Tools

Cloud Computing

Opportunity score

Medium opportunity (69%)

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The Problem

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.

Potential Solution

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.

Why Now?

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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Showing 1-20 of 133 signals

Job adsSep 10, 2026

bytedance

Senior Cloud Acceleration Engineer - DPU & AI Infrastructure (Multiple Positions)

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.

Job adsSep 9, 2026

Software Engineer, GenAI Frameworks

Design and implement scalable systems for distributed ML training and inference, including data ingestion pipelines, feature processing, and model serving infrastructure Develop and optimize ML platform components such as training orchestration, gradient communication, and checkpoint management across large-scale distributed environments

Job adsSep 4, 2026

bytedance

Research Engineer — Training Performance & ML Compilation (Torch Compile) - Seed Infra

- Develop and extend ML compilation capabilities based on the PyTorch compilation stack (e.g. FX, Dynamo, Inductor) to improve training efficiency across heterogeneous GPU platforms. - Conduct performance profiling and analysis of large-scale training jobs; identify and resolve bottlenecks in collaboration with research and infrastructure teams.

Job adsSep 3, 2026

amazon

Sr. SDE, Edge AI ML Platform, Edge AI and Science

- Design distributed training capabilities across data, tensor, pipeline, and model parallelism for large language and multimodal models. - Scale workflows on multi-node GPU clusters while improving training throughput, GPU utilization, memory efficiency, communication performance, failure recovery, and developer iteration time.

Job adsSep 2, 2026

twelve-labs

Senior ML Research Engineer, Multimodal Structure & Marengo

Architect and optimize large-scale training infrastructure, including distributed training pipelines, data processing systems, experiment workflows, and GPU utilization. Own production model APIs end to end, from model packaging and API design to inference optimization and reliable operation at scale.

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GPU? Cluster Optimization Platform for Distributed ML? Training