Software Engineer, Full-Stack — Developer Experience

In This Role, You Will

• Design, build, and operate CI infrastructure that gives engineers fast, reliable feedback on every change.
• Improve Bazel-based build and test workflows across a large, polyglot codebase, including dependency modeling, remote caching, remote execution, and build/test performance.
• Build systems that reduce unnecessary CI work through affected-target detection, test selection, caching, batching, and smarter scheduling.
• Partner closely with product and infrastructure teams to understand their workflows, pain points, and reliability needs, then turn those into practical platform improvements.
• Improve the observability and debuggability of build and CI failures, making it easier for engineers to distinguish product regressions, infrastructure failures, and flakes.
• Use modern AI tools to rethink how engineers interact with CI: failure explanation, fix suggestions, automatic retries, and agent-assisted debugging.
• Own the reliability of the systems you build, including participating in an on-call rotation for critical developer infrastructure.

Technologies Commonly Used

• Bazel and Starlark for large-scale build and test workflows
• Buildkite for CI orchestration
• Python and FastAPI for internal services
• Kubernetes for large-scale infrastructure
• Terraform for infrastructure as code
• Postgres, Kafka, and other systems used to power internal engineering platforms

You May Be A Strong Fit If You

• Have 5+ years of software engineering experience, including significant experience building infrastructure or tooling for developers.
• Have hands-on experience with Bazel, Buck, Pants, Gradle, or similar build systems, and understand the tradeoffs of hermetic builds, dependency graphs, caching, and remote execution.
• Have built or operated CI systems at scale, especially in environments where build time, queue time, test flakiness, and developer trust materially affect engineering velocity.
• Care deeply about developer experience and have empathy for the small sources of friction that slow teams down or create operational toil.
• Are comfortable debugging distributed systems and using metrics, logs, traces, and structured data to understand reliability and performance problems.
• Can work across teams, communicate clearly, and turn ambiguous productivity problems into concrete technical plans.
• Are excited to apply AI to developer infrastructure in ways that make engineers faster without weakening quality or safety.