Job Description

Vacancy Status: No: This posting is not for an existing vacancy within the organization and is open to new applications. (New Head Count)

AI Disclosure: As part of the application process, Artificial Intelligence will be used in the hiring process for this role.

Hybrid: This role is categorized as hybrid. This means the successful candidate is expected to report to Markham three times per week, at minimum.

We 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

Your 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
- Solid understanding of software engineering fundamentals, system design, APIs, data flows, observability, and production operations
- Experience integrating AI‑powered capabilities

📌 Staff Software Engineer — Ai For Developer Productivity (Markham)
🏢 General Motors
📍 Markham