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Chamber puts your AI infrastructure on autopilot, and saves your machine learning engineers hours of manual effort. Our agents continually monitor, detects failures, provide root-cause analysis, resolve issues, and optimize AI workloads and scale across clouds. It operates like an autonomous infrastructure team, helping save your research and engineers hours each day, and debugging workload performance issues. Your ML teams move faster, infra waste drops, and GPU bottlenecks disappear.
They diagnose failed training jobs, reclaim idle GPUs, and keep every node healthy — automatically when it's safe, with a human in the loop when it isn't — so researchers stay focused on building models, not fighting the cluster.
Previously Astraio
Second Time Founder with 1X Exit | Former Engineering Leader at Meta & Amazon | Ex-Microsoft. We are building to accelerate the world’s AI adoption and innovation.
Shipped observability and infrastructure products at scale across AWS and Amazon. At AWS, I launched CloudWatch Application Signals, the native APM solution for AWS. At Amazon, I led the launch of the central GPU orchestration service, centralizing access and maximizing utilization across Amazon's science and AI/ML teams.
I’m a software engineer from Malaysia who moved to the U.S. in 2016. I’ve built high-impact systems at Amount, Avant, Flexport, and Amazon. I’ve worked across fintech, logistics, and GPU-related scheduling tooling, where I saw how hard distributed training is for many teams. I’m now co-founder of Chamber, focused on simplifying GPU orchestration for training workloads.
I am a cofounder of Chamber and a former Senior Software Engineer at Amazon. Over the past 9+ years, I’ve built and launched multiple 0→1 AWS products, with deep expertise in large-scale observability, distributed systems, and AI infrastructure efficiency. At Chamber, I’m applying this experience to build intelligent AI workload orchestration and observability software that helps companies run AI workloads much more efficiently.
Founded 2026 · Source
Source-based assessment · automated evidence rules
The retained text does not establish product availability or customer use.
Maturity is an evidence-based assessment, not a success rating. It may understate the current stage where evidence is limited. Coverage records documented areas, not a complete or independently audited company history.
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