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ClusterPilot GPU Training Optimizer
45 Signals

ClusterPilot GPU Training Optimizer

A SaaS control plane that profiles distributed ML training jobs and automatically recommends parallelism, batching, and GPU utilization fixes.

Added May 25, 2026

AI Infrastructure
MLOps
Cloud Optimization
Opportunity score

Low opportunity (47%)

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The Problem

Teams building large-scale multimodal and deep learning systems struggle to keep distributed training and inference efficient across GPU and TPU clusters. Job postings repeatedly point to pain around model parallelism, data parallelism, pipeline parallelism, communication overhead, GPU-aware loading, and training/serving co-design.

Potential Solution

ClusterPilot connects to existing training pipelines and cluster telemetry to identify bottlenecks in GPU utilization, communication, batching, data loading, and parallelism strategy. It provides job-level diagnostics, configuration recommendations, and automated experiment plans for improving throughput across PyTorch, JAX, GPU, and TPU environments.

Why Now?

AI teams are scaling models across larger distributed clusters, making manual performance tuning increasingly expensive and specialized. The repeated hiring demand for ML infrastructure engineers focused on training and inference optimization suggests this is an urgent operational problem, not a theoretical one.

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Showing 1-20 of 45 signals

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 adsAug 31, 2026
pluralis-research
Research Engineer - Pre-training

Run instrumentation: Build the monitoring that shows throughput, bottlenecks, and model quality across hundreds of devices. Hands-on distributed training (required): You've trained models across many devices in PyTorch with FSDP, DeepSpeed, Megatron, or your own implementation. You understand data, tensor, and pipeline parallelism.

Job adsAug 30, 2026
amazon
Sr GenAI Infra Specialist SA, AWS WWSO Startup

- Provide deep technical guidance on training optimization distributed training strategies, framework selection (PyTorch, JAX, NeMo), SageMaker HyperPod, Slurm/PCS integration, checkpointing, and data pipeline design - Guide customers on GPU and accelerator profiling identifying bottlenecks (compute, memory, I/O), optimizing utilization, and tuning system-level performance

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