Cloud Quality Engineer

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 what its blast radius is. Test the change's actual impact, not its surface area.

- Automation Expansion: Increase automation coverage continuously. Fix flaky tests rather than tolerate them. Use AI tooling effectively to accelerate test creation, debugging, and analysis.

- Quality Discipline: Choose high-value tests over volume metrics. Drive the team's standards for what "tested" means and what "ready to ship" means.

- Cross-Team Operation: Work with platform, ML, infrastructure, and product teams across timezones. Influence quality outcomes without owning every team's roadmap.

Skills & Qualifications

- 5+ years of experience in quality engineering, test engineering, or a closely related role, with substantial individual contributor experience on large-scale distributed systems or cloud infrastructure.

- Deep cloud platform experience, preferably AWS - networking, compute orchestration, container platforms, and multi-region production services. You can reason about what is happening at the cloud layer when something fails.

- Track record of building scalable test infrastructure - frameworks, harnesses, environments, and automation that scale with the system under test rather than fighting it.

- Strong systems debugging and reasoning. You can take an unfamiliar failure and follow it through layers of the stack to a root cause.

- Strong proficiency in at least one backend language (Python, Go, or C++), sufficient to read production code, write production-grade tests, and contribute infrastructure code directly.

- Excellent written and async communication. You operate effectively across time zones and in environments where most decisions get made in writing.

- Self-direction under ambiguity. You frame problems, make trade-off decisions, and push back when quality is at risk - without waiting to be asked.

- Experience with Cloud infrastructure, model serving systems, or GPU accelerated workloads is a strong plus.

- Experience using AI tooling (LLMs, coding assistants, agents) to accelerate test development, triage, or analysis is a plus.