HireMO

Remote/Hybrid

Milford, Michigan, United States of America

Remote - United States

Austin, Texas, United States of America

Mountain View, California, United States of America

Full time

JR-202620964

Job Description

The Role:

General Motors is seeking a software engineering leader to modernize and scale the build systems that support software-defined vehicles. This role will help lead the transition from a legacy Make-based system to Bazel, Google's open-source build tool, improving speed, reliability, and robustness across the engineering organization.The successful candidate will drive this capability at scale, influence technical direction across teams, and help establish lofty platform capability goals.

What You'll Do:

+ Lead software engineering productivity and effectiveness by identifying opportunities, shaping strategies, improving processes, guiding change, and connecting initiatives to deliver greater value through innovation, learning, and simplification.

+ Lead high-impact efforts hands-on, solve complex and unusual problems, and communicate effectively as a coach and mentor while balancing technical command with empathy.

+ Modernize a large embedded-software build and test system by transitioning legacy Make- or shell-based workflows to Bazel and establishing scalable, reproducible, secure, and developer-friendly infrastructure across repositories, toolchains, CI/CD, testing, and release processes.

+ Assess current build, test, dependency, toolchain, and hardware workflows to define a phased Bazel migration strategy and rollout model.

+ Design and implement reusable Bazel rules, macros, toolchains, platforms, configuration patterns, and repository standards, while migrating embedded applications, libraries, generated code, firmware, cross-compilation flows, and platform variants with validated build parity and reproducibility.

+ Integrate Bazel with enterprise CI/CD, testing, release, artifact, deployment, observability, security, and infrastructure workflows, and optimize the platform through caching, parallelism, reliability practices, documentation, training, legacy Make retirement, and continuous improvement.

+ Measure success through target repositories migrated with validated build and test parity; faster clean and incremental builds, CI queues, and developer feedback; increased reproducibility, approved hermetic-toolchain adoption, cache effectiveness, and CI capacity; and lower build, pipeline, and flaky-test rates.

+ Improve diagnosis and recovery time while ensuring complete artifact provenance, test evidence, and security-policy compliance for releases, strong developer adoption, reduced support burden, and measurable productivity improvement.

+ Hold a bachelor's degree in software engineering or have equivalent experience, along with 15 years of software development experience.

+ Bring a deep understanding of real-time software architecture and experience shipping high-quality, safety-critical products in automotive, embedded, safety-related, calibration, or systems-engineering environments.

+ Demonstrate exceptional problem-solving and analytical abilities, keen attention to detail, and the ability to manage multiple projects and priorities in fast-paced, action-oriented decision-making environments.

+ Bring strong expertise building and maintaining production software systems, proficiency in Python and at least one additional engineering-tooling language, and experience with AI-assisted developer productivity, coding agents, code intelligence, knowledge retrieval, or workflow orchestration.

+ Evaluate AI-enabled engineering workflows using measurable quality, adoption, and productivity outcomes.

Your Skills & Abilities (Required Qualifications):

+ Bachelor's degree in software engineering or or equivalent experience

+ 10+ years of experience in software development

+ Deep understanding of software architecture for real time systems

+ Experience shipping high-quality safety critical products

+ Exceptional problem-solving and analytical abilities, with a keen attention to detail

+ Demonstrated ability to manage multiple projects and priorities in a fast-paced environment.

+ Demonstrated ability to work in an action-oriented decision-making environment

+ Strong expertise building and maintaining production software systems, with proficiency in Python and at least one additional language used in engineering tooling environments

+ Automotive, embedded, safety-related, calibration, or systems-engineering environments.

+ Large-scale migration from Make or shell-based build orchestration to Bazel.

+ AI-assisted developer productivity, coding agents, code intelligence, knowledge retrieval, or workflow orchestration.

+ Evaluation of AI-enabled engineering workflows using measurable quality, adoption, and productivity outcomes.

Key Deliverables:

+ Assess current Make-based build, test, dependency, toolchain, and hardware workflows, then define a phased Bazel migration strategy and rollout model.

+ Design and implement reusable Bazel rules, macros, toolchains, platforms, configuration patterns, and repository standards for embedded software.

+ Migrate embedded applications, libraries, generated code, firmware, cross-compilation flows, and platform variants while ensuring build parity and reproducibility.

+ Integrate Bazel with enterprise CI/CD, testing, release, artifact, deployment, observability, security, and infrastructure workflows.

+ Optimize and operationalize the platform through caching, parallelism, reliability practices, documentation, training, legacy Make retirement, and continuous improvement.

Measures of Success:

+ Target repositories migrated with validated build and test parity.

+ Reduced clean and incremental build time, CI queue time, and developer feedback time.

+ Increased reproducibility, approved hermetic-toolchain adoption, cache effectiveness, and CI capacity.

+ Lower build, pipeline, and flaky-test rates, with improved diagnosis and recovery time.

+ Complete artifact provenance, test evidence, and security-policy compliance for releases.

+ Strong developer adoption, reduced support burden, and measurable productivity improvement.

What Will Give You A Competitive Edge (Preferred Qualifications):

+ Master's degree or PhD in Computer Science, Software Engineering, Machine Learning, AI, or a related field

+ Experience in embedded software development, automotive software, systems engineering, or safety-related toolchains

+ Familiarity with GM engineering tools, engineering workflows, or internal platform environments

+ Experience building AI assistants, coding agents, or domain-specific AI capabilities for engineers

+ Experience with knowledge systems, vector search, ranking, workflow orchestration, or code intelligence platforms

+ Experience supporting large engineering communities through reusable tools, templates, and automation

+ Experience evaluating AI quality ...

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