Senior Build Systems & Pipeline Engineer

Adobe•San Jose, CA

About The Position

We are looking for a Senior Build Systems & Pipeline Engineer to help scale and evolve the infrastructure that powers development and delivery across Adobe Express across web, services, iOS, and Android platforms. This role sits at the intersection of developer productivity, platform engineering, CI/CD architecture, observability, and intelligent developer tooling. You will design and operate high-performance build systems and resilient delivery pipelines that enable teams to build, test, validate, and release complex multi-platform applications efficiently and reliably. You will partner closely with client, services, infrastructure, and developer experience teams to improve engineering velocity, reduce build and test latency, increase pipeline reliability, and strengthen operational visibility across the software delivery lifecycle. We are also looking for engineers who actively leverage modern AI-assisted development workflows and understand how intelligent tooling is reshaping software engineering practices, developer experience, and delivery systems at scale.

Requirements

BS/MS in Computer Science or equivalent practical experience
6+ years of experience in build systems, CI/CD infrastructure, or developer platform engineering
Deep expertise with Bazel build systems in large-scale production environments
Strong experience designing and operating dynamic pipelines using platforms such as Buildkite
Experience supporting large-scale web and mobile application development environments
Strong understanding of build graph optimization, dependency management, caching strategies, and reproducible builds
Experience building reliable automated testing infrastructure across frontend, backend, and mobile systems
Strong understanding of system observability, metrics, monitoring, telemetry, tracing, and operational health analysis
Experience with cloud-native infrastructure and scalable distributed systems
Proficiency in one or more programming languages such as Python, Rust, Kotlin, Swift, or TypeScript
Strong practical understanding of modern AI-assisted software engineering workflows and tools
Experience using AI-assisted development environments such as Cursor, Claude Code, GitHub Copilot, or equivalent tooling in day-to-day engineering workflows
Ability to effectively guide, constrain, and evaluate AI-generated output through structured prompting, iteration, validation, and engineering review practices
Experience applying AI tooling to workflow automation, debugging, incident analysis, code generation, testing, documentation, or developer experience improvements
Understanding of where AI-assisted workflows are effective — and where engineering judgment, validation, and human review remain critical
Strong communication and collaboration skills across distributed engineering teams

Nice To Haves

Experience with remote execution environments and distributed build systems
Familiarity with monorepo tooling and large-scale source management strategies
Experience with release automation and deployment orchestration at scale
Experience improving developer experience and internal engineering platforms
Experience operationalizing AI-enabled engineering workflows in large engineering organizations
Familiarity with agentic developer workflows, multi-step automation systems, or AI- assisted operational tooling
Experience designing repositories, documentation, and development conventions optimized for AI-assisted engineering workflows

Responsibilities

Architect, implement, and maintain scalable build systems for large multi-platform codebases spanning web, services, iOS, and Android
Design and optimize Bazel-based build infrastructure for performance, reliability, and reproducibility
Build and operate dynamic CI/CD pipelines using platforms such as Buildkite and GitLab
Develop stable and scalable automated test pipelines for web applications, backend services, and mobile platforms
Improve build and test observability through metrics, telemetry, dashboards, alerting, tracing, and pipeline health analysis
Drive improvements in build performance, developer feedback loops, release velocity, and infrastructure reliability
Develop tooling and automation supporting testing, release orchestration, artifact management, and deployment workflows
Partner with engineering teams to streamline developer workflows and improve day-to-day engineering productivity
Enable scalable remote caching and distributed build execution strategies
Apply AI-assisted development tools and workflows to improve engineering productivity, debugging, code quality, testing, and operational efficiency
Evaluate and integrate emerging AI-enabled engineering workflows into development environments and CI/CD systems
Design engineering systems and repositories that work effectively with AI-assisted developer tooling through strong conventions, documentation, metadata, and workflow design
Contribute to long-term platform strategy around build infrastructure, developer velocity, observability, and intelligent automation