Description The Role General Motors' Advanced Security Engineering team develops advanced security capabilities to help secure GM and reduce real security exposure. We leverage artificial intelligence (AI) capabilities to strengthen defenses, validate weaknesses, and support authorized offensive security testing in controlled environments. By combining security findings with code, cloud, and runtime context, we identify issues that are real and reachable and help deliver remediation, mitigation, or detection.
You will design, build, and operate the cloud services and AI agents that support this work. These systems analyze untrusted code, handle sensitive security data, and give agents controlled access to security tools and computing environments. Designing that access safely is a core part of the role.
As a senior individual contributor, you will work independently with minimal guidance, turn broad challenges into practical engineering solutions, and own delivery across team boundaries. You will evaluate options, exercise sound technical judgment when information is limited, and bring your work through review at key decision points. You will be accountable for the reliability, security, and cost of the systems you deliver.
This is an opportunity to help shape a new team and build new security capabilities at GM. You will help define what to build, not only how to build it, while collaborating with security specialists and mentoring other engineers.
What You'll Do - Design, build, and operate production cloud services in Python, including containerized workloads, event-driven processing, and data storage. Define and manage the supporting infrastructure as code.
- Integrate findings and context from security tools, source control, and cloud application programming interfaces (APIs). Build AI agents that triage findings, gather evidence from source code and cloud configuration, and propose fixes.
- Build and harden sandboxed environments for AI-enabled testing of authorized targets. Apply defense in depth, designing for the possibility that any individual containment boundary can fail.
- Control agent access through isolation, least-privilege identities, network egress limits, and secrets management. Separate agents that produce results from those that verify them. Treat source code, tool output, and model responses as untrusted input, defend against prompt injection, and keep sensitive data out of prompts and logs.
- Evaluate model accuracy, cost, and latency to inform model selection and detect regressions. Use evidence to explain technical trade-offs and guide improvements to the systems you own.
- Partner with application security, penetration testing, and cloud security engineers to turn expert judgment into repeatable checks and deliver solutions across teams. Communicate technical risks and recommendations to non-specialists, review code, and mentor less-experienced engineers through constructive feedback and collaborative problem-solving.
Your Skills & Abilities (Required Qualifications) - Bachelor's degree in Computer Science, Cybersecurity, Engineering, or a related field, or equivalent practical experience.
- 5+ years of experience in software engineering, cloud engineering, or security engineering.
- Strong experience writing production Python.
- Experience with infrastructure as code, such as Terraform, and continuous integration and continuous delivery (CI/CD) on a major cloud platform.
- Working knowledge of application security, including common vulnerability classes, how security scanners work, and where they produce false positives.
- Experience building secure systems using least privilege, secrets management, isolation, and safe handling of untrusted input.
- Ability to own work across teams, make sound decisions with limited information, and explain technical risk to people who are not security specialists.
- Experience using agentic large language model (LLM) tools and solutions.
What Will Give You a Competitive Edge (Preferred Qualifications) - Experience building applications with large language models and agent frameworks, including tool use and output evaluation.
- Experience with containers and Kubernetes.
- Experience with sandboxing and isolation, or securing systems that execute untrusted code.
- Experience with static or program analysis, such as writing Semgrep or CodeQL rules.
- Experience in vulnerability research, penetration testing, or exploit development.
- Familiarity with AI security risks and controls, such as the Open Worldwide Application Security Project (OWASP) Top 10 for LLM Applications.
- Public examples of shipped work, such as open-source contributions, published tools, or technical write-ups.
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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}.
Relocation benefits are available for candidates who qualify under company policy.
About GMOur vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.
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