Chamber

The AIOps Agent for ML Teams

Y CombinatorWinter 2026ActiveB2BInfrastructure

What they do

Problem

ML teams waste significant time and money manually monitoring, debugging, and optimizing AI infrastructure and GPU workloads across clouds.

Solution

Chamber provides autonomous AIOps agents that monitor AI infrastructure, detect failures, perform root-cause analysis, resolve issues, and optimize and scale ML workloads across clouds.

Who it's for

Machine learning engineers and ML platform/infra teams at organizations running production AI workloads.

Founders

Charles Ding

Founder & CEO

Second Time Founder with 1X Exit | Former Engineering Leader at Meta & Amazon | Ex-Microsoft. We are building to accelerate the world’s AI innovation through agents.

Andreas Bloomquist

Founder

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.

Jason Ong

Founder

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.

Shaocheng Wang

Founder

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.