Job DescriptionWe are seeking a highly skilled Staff Software Engineer to join the Virtualization & Embedded Software Development Tools organization.
In this role, you will apply artificial intelligence to improve developer productivity, modernize engineering workflows, and advance toolchain capabilities across embedded software development. You will shape and deliver practical, production-grade AI capabilities that help engineers build, test, analyze, troubleshoot, and support complex software systems more effectively at scale.
This role is ideal for a recognized technical expert who thrives in ambiguity, works independently with broad latitude, influences key technical decisions, and leads large cross-functional efforts with broad visibility. You will partner across CI/CD, virtualization, systems engineering, calibration, platform, and software development teams to identify high-value opportunities and turn them into scalable solutions that improve engineering throughput, reliability, and user experience.
Our organization supports the end-to-end engineering toolchain that enables teams to define, develop, validate, calibrate, and release embedded software and systems. That includes engineering tools, build and test workflows, dashboards, automation, integrations, and engineering support platforms. As part of this team, you will help define how AI can be used responsibly and effectively in real engineering environments to improve speed, quality, and user experience.
What you'll do- Define the technical vision for AI-powered developer productivity capabilities across engineering tools and workflows
- Design, develop, and deliver AI-powered solutions that reduce manual effort, accelerate issue resolution, and improve software quality across development, debugging, test analysis, issue triage, documentation, and engineering support workflows
- Partner with cross-functional teams to identify high-value AI use cases and translate them into scalable products, platforms, and reusable capabilities
- Integrate AI-powered capabilities into engineering tools, workflows, and automation platforms in ways that improve reliability, usability, and adoption
- Lead architecture and implementation decisions for AI systems spanning model access, orchestration, retrieval, evaluation, observability, security, and enterprise integration
- Drive productionization of AI capabilities within GM engineering environments, including cloud-hosted services, internal platforms, CI/CD systems, and developer tools
- Establish technical standards and best practices for responsible use of AI in engineering tools, including quality, traceability, maintainability, and cybersecurity considerations
- Serve as a subject matter expert and technical leader across organizational boundaries, influencing roadmaps, solution direction, and implementation priorities
- Mentor engineers on AI system design, prompt and workflow design, evaluation strategies, and toolchain integration without formal people-leader responsibility
- Present strategy, progress, recommendations, and demonstrations to technical leaders and partner organizations
Additional job descriptionYour skills and abilities (required qualifications)- Bachelor's degree in Computer Science, Software Engineering, Electrical Engineering, Computer Engineering, or a related technical field
- 10+ years of experience in software engineering, developer tooling, platform engineering, machine learning engineering, applied AI, or a closely related field
- Strong expertise building and shipping production software systems, with proficiency in Python and at least one additional language used in engineering tooling environments
- Demonstrated expertise applying AI and LLM-based approaches to engineering problems such as code analysis, workflow automation, knowledge retrieval, summarization, troubleshooting, or developer productivity support
- Strong understanding of software engineering fundamentals, system design, APIs, data flows, observability, and production operations
- Experience integrating AI-powered capabilities into enterprise platforms, engineering tools, or CI/CD systems
- Experience with cloud services, containerization, and orchestration technologies
- Strong knowledge of secure engineering practices and responsible AI guardrails
- Demonstrated success leading technically ambiguous, cross-functional efforts from concept through production deployment
- Excellent communication skills and the ability to influence technical direction across teams without formal authority
- Experience with developer platforms, build systems, testing systems, or internal engineering tools
- Experience balancing fast experimentation with production reliability, maintainability, and compliance
What can 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
- Experience with CI/CD platforms, build and test orchestration, and software quality automation
- 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
- Experience designing retrieval-augmented or tool-using AI workflows
- Experience integrating AI into GitHub-based engineering workflows or related enterprise automation pipelines
Why join usThis is an opportunity to shape how AI is applied in real-world engineering environments at scale. You will work on high-value problems at the intersection of developer productivity, toolchain modernization, automation, CI/CD, observability, and embedded software development. Your work will help engineering teams move faster, reduce friction, improve quality, and unlock new capabilities across a critical part of GM's software development ecosystem.
You will join a team that is actively modernizing and scaling core engineering toolchains, including CI/CD robustness, workflow automation, dashboarding and observability, configuration and calibration workflows, and platform evolution. This role offers the opportunity to turn promising AI concepts into reliable, enterprise-ready capabilities that deliver measurable value.
Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position, as well as geography of the selected candidate.
The salary range for this role is ($160,200 - 246,300). The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
Benefits:
Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.
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GM does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc).This role is categorized as hybrid. This means the selected candidate is expected to report to a specific location at least 3 times a week {or other frequency dictated by their manager}.This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate.
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