Staff Software Engineer, AI for Developer Productivity

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 doDefine the technical vision for AI-powered developer productivity capabilities across engineering tools and workflowsDesign, 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 workflowsPartner with cross-functional teams to identify high-value AI use cases and translate them into scalable products, platforms, and reusable capabilitiesIntegrate AI-powered capabilities into engineering tools, workflows, and automation platforms in ways that improve reliability, usability, and adoptionLead architecture and implementation decisions for AI systems spanning model access, orchestration, retrieval, evaluation, observability, security, and enterprise integrationDrive productionization of AI capabilities within GM engineering environments, including cloud-hosted services, internal platforms, CI/CD systems, and developer toolsEstablish technical standards and best practices for responsible use of AI in engineering tools, including quality, traceability, maintainability, and cybersecurity considerationsServe as a subject matter expert and technical leader across organizational boundaries, influencing roadmaps, solution direction, and implementation prioritiesMentor engineers on AI system design, prompt and workflow design, evaluation strategies, and toolchain integration without formal people-leader responsibilityPresent 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 field10+ years of experience in software engineering, developer tooling, platform engineering, machine learning engineering, applied AI, or a closely related fieldStrong expertise building and shipping production software systems, with proficiency in Python and at least one additional language used in engineering tooling environmentsDemonstrated expertise applying AI and LLM-based approaches to engineering problems such as code analysis, workflow automation, knowledge retrieval, summarization, troubleshooting, or developer productivity supportStrong understanding of software engineering fundamentals, system design, APIs, data flows, observability, and production operationsExperience integrating AI-powered capabilities into enterprise platforms, engineering tools, or CI/CD systemsExperience with cloud services, containerization, and orchestration technologiesStrong knowledge of secure engineering practices and responsible AI guardrailsDemonstrated success leading technically ambiguous, cross-functional efforts from concept through production deploymentExcellent communication skills and the ability to influence technical direction across teams without formal authorityExperience with developer platforms, build systems, testing systems, or internal engineering toolsExperience 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 fieldExperience in embedded software development, automotive software, systems engineering, or safety-related toolchainsExperience with CI/CD platforms, build and test orchestration, and software quality automationFamiliarity with GM engineering tools, engineering workflows, or internal platform environmentsExperience building AI assistants, coding agents, or domain-specific AI capabilities for engineersExperience with knowledge systems, vector search, ranking, workflow orchestration, or code intelligence platformsExperience supporting large engineering communities through reusable tools, templates, and automationExperience evaluating AI quality in production systems using measurable outcomes such as acceptance rate, time saved, precision and recall, hallucination reduction, or workflow completion rateExperience designing retrieval-augmented or tool-using AI workflowsExperience 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 pro