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.
The role
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.
- Proven experience designing, developing, and deploying agentic AI models and autonomous, tool-using AI systems in production 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
Compensation:
The salary range for this role is $147,000to $196,600. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
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
Benefits:
The goal of the General Motors of Canada total rewards program is to support the health and well-being of you and your family. Our comprehensive compensation plan currently includes the following benefits, in addition to many others:
- Paid time off including vacation days, holidays, and supplemental benefits for pregnancy, parental and adoption leave.
- Healthcare, dental and vision benefits including health care spending account and wellness incentive.
- Life insurance plans to cover you and your family.
- Company and matching contributions to a Defined Contribution Pension plan to help you save for retirement.
- GM Vehicle Purchase Plan for you, your family, and friends.
