Staff Software Engineer, Build Systems (Bazel)

GRAIL is seeking a Build Engineer to join our DevOps / Platform Engineering organization. Our team builds and operates the infrastructure, tooling, and developer platforms that engineering and research teams rely on to move quickly and safely, from local development environments through builds, tests, CI/CD, and release.

In this role, you will focus on developer experience: reducing friction in day-to-day engineering workflows and making complex, multi-language systems easier to build, test, and maintain. You will own and improve build-system workflows in our Bazel-based monorepo, raise CI reliability and performance, and build internal tooling and self-service automation used across engineering and research. This is a high-leverage role with broad visibility, your work directly accelerates teams working on early cancer detection.

This role is based in Sunnyvale, California. This role offers a flexible work arrangement, with the ability to work from GRAIL's office or from home. Our current flexible work arrangement policy requires that a minimum of 60%, or 24 hours, of your total work week be on-site. Your specific schedule, determined in collaboration with your manager, will align with team and business needs and could exceed the 60% requirement for the site. At our Sunnyvale campus, Tuesdays and Thursdays are the key days where we encourage on-site presence to engage in events and on-site activities.

Responsibilities

• Own and improve build-system workflows in our Bazel-based monorepo, including build rules, test execution, dependency management, caching, and build diagnostics.
• Improve CI performance, reliability, and observability, including failure diagnostics, flaky-test handling, and pipeline maintainability.
• Partner with engineering and research teams to identify and remove friction in local development, builds, tests, CI, release workflows, and tooling.
• Build and maintain internal tools, automation, and self-service workflows that reduce repeated manual effort for engineers.
• Diagnose complex build and CI failures across Go, Python, TypeScript, and Java codebases.
• Collaborate with DevOps and platform engineers on AWS and Kubernetes infrastructure where it intersects with build, test, and delivery workflows.
• Establish practical standards, documentation, and reusable patterns for build systems, CI pipelines, and developer tooling.
• Take accountability for ambiguous developer-productivity problems and drive them through to measurable improvements.
• Use modern AI coding agents responsibly to explore unfamiliar codebases, investigate difficult failures, prototype solutions, and accelerate tooling work.

These responsibilities summarize the role's primary responsibilities and are not an exhaustive list. They may change at the company's discretion.

Required Qualifications

• + 6years of experience working on build systems, CI/CD, or developer productivity / platform tooling.
• 2+ years of hands-on experience with a large-scale build system (for example Bazel, Buck, Pants, Gradle, or CMake).
• 2+ years of experience building and maintaining CI/CD pipelines in GitLab CI, GitHub Actions, Jenkins, or a comparable system.
• 3+ years of professional coding experience in one or more of Go, Python, TypeScript, or Java.
• 1+ years of hands-on experience with AWS and containerized workloads on Kubernetes.
• BS in Computer Science or a related technical field, or equivalent experience.

Preferred Qualifications

• Production Bazel experience, especially in a large, multi-language monorepo.
• Experience with Bazel rules and toolchains, remote caching, remote execution, test sharding, or build performance optimization.
• Experience with dependency management, hermetic and reproducible builds, and test infrastructure at scale.
• Experience improving developer experience in a mixed-language engineering environment (Go, Python, TypeScript, Java).
• Experience building internal CLIs, automation, release tooling, or self-service developer platforms.
• Experience defining and using developer-productivity metrics to prioritize work and demonstrate impact.
• Experience diagnosing and reducing CI flakiness, build nondeterminism, and long-tail pipeline failures.
• Experience supporting scientific, research, or data-intensive teams, including Python packaging and container image workflows.
• Strong debugging skills across complex, multi-team build, test, and pipeline failures.
• Clear communication and collaboration skills, with a track record of working directly with developers, researchers, and platform engineers.
• Comfortable using modern AI coding agents as part of day-to-day engineering work while applying sound engineering judgment.
• Experience mentoring engineers or driving adoption of tooling and standards across teams.