Staff Software Engineer- Test System Modernization

GM•Milford, MI

About The Position

General Motors is seeking a Staff Software Engineer to shape and scale the engineering platforms that build, test, integrate, validate, and deliver embedded software for software-defined vehicles. This role is for a hands-on technical leader who can turn ambiguous, cross-team problems into durable architecture, practical standards, and measurable improvements in engineering effectiveness, release confidence, and product quality. The successful candidate will lead a major capability across multiple teams, influence technical direction beyond their immediate organization, and connect software, tooling, infrastructure, test, and release activities into a coherent engineering system. The role involves leading the modernization of a large-scale embedded software test system, defining and modernizing a standardized, fully automated, headless framework across virtual and physical environments to enable full-suite testing multiple times per day.

Requirements

Bachelor's degree in computer science, computer engineering, electrical engineering, software engineering, systems engineering or a related technical field; or equivalent practical experience.
Typically, 10 or more years experience validating software systems, including substantial experience leading architecture across organizations.
Deep understanding of software architecture for real time systems.
Experience shipping high quality safety critical products.
Experience architecting large-scale CI/CD, embedded test automation, and build-to-test pipelines.
Expertise in variation management, configuration comparison, change-impact analysis, and risk-based regression testing.
Experience with test-result data modeling, Databricks, analytics, data mining, and API development.
Proficiency with embedded systems, Linux, Git, Python, Bash, Robot Framework, containers, and infrastructure as code.
Demonstrated ability to integrate automated diagnostics and issue-management workflows.
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.

Nice To Haves

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 in production systems using measurable outcomes such as acceptance rate, time saved, precision and recall, hallucination reduction, or workflow completion rate.

Responsibilities

Be a critical leader of software engineering productivity and effectiveness.
Identify opportunities, strategies, process improvements, and lead the change.
Be hands-on by leading high impact efforts.
Be a strong communicator, coach, and mentor.
Analyze unusual and difficult problems effectively.
Ability to connect multiple initiatives together to deliver higher value.
Visualize required innovation, learning and simplification in this space and transition the team to execute what is required to achieve.
Maintain command skills and empathy at the same time.
Lead the modernization of a large-scale embedded software test system.
Define and modernize a standardized, fully automated, headless framework across virtual and physical environments to enable full-suite testing multiple times per day.
Define the architecture and roadmap for standardized test execution, configuration and variation management, traceability, quality gates, regression selection, and results reporting.
Establish change-impact analysis and risk-based regression strategies using Gears, Pure Variants, or equivalent tools to determine test applicability and optimize re-testing.
Standardize test-result schemas, metadata, diagnostics, and artifacts; enable storage in Databricks for analytics, trend analysis, data mining, and reporting.
Develop APIs and integrations connecting test execution, test management, issue tracking, failure triage, and diagnostic tools.
Improve test robustness, failure triage, execution speed, and deployment efficiency across CI/CD pipelines and physical and virtual test environments.