Cloud Quality Engineer

Cerebras; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Cerebras Systems is seeking a Cloud Quality Engineer to own the weekly cloud release quality end to end and to build scalable test infrastructure. This is a hands-on senior IC role for someone who treats quality as a first‑class engineering problem - not a downstream gate.

You will drive every release from branch cut to sign‑off, build scalable test infrastructure that grows with customer load, and push back when quality is at risk.

Responsibilities

• Release Quality Ownership: Drive weekly cloud release qualification end to end. Read every PR in the release branch first‑hand; understand what changed; decide where the risk is; and design the qualification that exercises the actual risk. Be the final voice before a release ships.
• Test Infrastructure at Scale: Build and evolve the test infrastructure - functional, integration, performance, and fault for the Inference Cloud platform. Plan for 20x growth in coverage, environments, and traffic.
• End‑to‑End System Understanding: Reason through the full stack — client SDK, API, gateway, inference software, driver, hardware. Know enough to debug from any layer and to test the right thing.
• Code Review with Intent: Read and review developer PRs with genuine understanding of what each change does and

Job description

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

About the team

The Cloud Quality team is responsible for the confidence behind every production release shipped to Cerebras Inference Cloud. We work closely with platform, infrastructure, ML systems, and product engineering teams to ensure that rapid iteration never comes at the expense of customer trust. Our environment spans distributed cloud systems, multi-region deployments, APIs, orchestration layers, and hardware-backed inference services. We are scaling quickly. The systems are growing in complexity, traffic is increasing rapidly, and release velocity remains high. We need engineers who can build quality systems that scale with the business.

About the role

We are hiring a Cloud Quality Engineer to own the quality of our weekly cloud releases end to end and to build the test infrastructure that lets the team scale. This is a hands‑on senior IC role for someone who treats quality as a first‑class engineering problem - not a downstream gate. You will drive every release from branch cut to sign‑off, build scalable test infrastructure that grows with customer load, and push back when quality is at risk. You will operate effectively across timezones, async‑first, with clear written communication. This is a role for someone who self‑drives. You will frequently work without complete product specifications, decide under ambiguity, and ask the right questions before code lands - not after.

Responsibilities

• Release Quality Ownership: Drive weekly cloud release qualification end to end. Read every PR in the release branch first‑hand; understand what changed; decide where the risk is; and design the qualification that exercises the actual risk. Be the final voice before a release ships.
• Test Infrastructure at Scale: Build and evolve the test infrastructure - functional, integration, performance, and fault for the Inference Cloud platform. Plan for 20x growth in coverage, environments, and traffic. Today's setup will not survive tomorrow's load; design for the next horizon.
• End‑to‑End System Understanding: Reason through the full stack — client SDK, API, gateway, inference software, driver, hardware. Know enough to debug from any layer and to test the right thing.
• Code Review with Intent: Read and review developer PRs with genuine understanding of what each change does and