Security Engineer
Listed on 2026-03-01
-
Engineering
AI Engineer, Cybersecurity -
IT/Tech
AI Engineer, Cybersecurity, Machine Learning/ ML Engineer
About Rivian
Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract.
As a company, we constantly challenge what’s possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations.
Role SummaryAs a Security Software Engineer at Rivian, you will sit at the intersection of cybersecurity, software engineering, and artificial intelligence. Your role is not just to “guard” the AI; it is to engineer the solutions that allow our AI/ML teams to move fast without breaking safety.
You will act as a bridge—writing the code that secures our infrastructure while partnering directly with data scientists and ML engineers to validate new systems before they touch production. You will play a key role in maturing Rivian’s AI security posture from “ad-hoc” to “systematic.” We are open to location on this role and report to our Sr. Manager, Cybersecurity.
ResponsibilitiesCollaborative Design & Validation (The “Consultant-Builder”)
Security Reviews: Partner with AI product teams during the design phase to review architecture. You will ask the hard questions: Where is this data stored? How is this model isolated? What happens if the prompt is malicious?
Threat Modeling: Participate in (and eventually lead) threat modeling sessions for new ML features. You will help teams identify risks in their RAG (Retrieval-Augmented Generation) pipelines or autonomous training loops.
Validation: Don’t just trust the design; verify it. Work with QA and Engineering to validate that security requirements (like auth
Z scopes or encryption) are actually implemented correctly in the final code.
Engineering & Tooling (The “SWE” Core)
Build “Paved Roads”: Write Python or Go libraries that make doing the “secure thing” the “easy thing” for data scientists. (e.g., a library that automatically handles token encryption for ML jobs).
Automate Compliance: Instead of asking teams to fill out spreadsheets, build automation that scans our Kubeflow/MLflow clusters to verify they meet security baselines.
Secure Code Contribution: Submit Pull Requests directly to ML repositories to fix vulnerabilities or harden logic, rather than just filing tickets for others to fix.
Maturing AI Security (The “Growth” Aspect)
Define Standards: Help write the “Gold Standard” documentation for how to deploy a secure model will turn tribal knowledge into engineering standards.
Vulnerability Management: Assist in triaging findings from bug bounties or internal scans related to our AI surface, and track the “Time to Remediate” to help us understand our maturity gaps.
Research & Prototyping: Stay ahead of the curve. Spend time researching new AI attacks (like Model Inversion) and prototype defenses to see if they work in our environment.
Must-Haves:
Strong Engineering Foundation: 2+ years of software engineering experience. You write clean, tested code (Python preferred) and understand the SDLC.
Security Mindset: Experience looking at a system design and identifying where it might break. You understand concepts like “Least Privilege,” “Defense in Depth,” and “Input Validation.”
Communication
Skills:
You can explain a security risk to a Data Scientist without using jargon, and you can explain an ML constraint to a Security Engineer.Understanding of AI/ML: You understand the basic components of an AI system (Data Lake → Training → Model Registry → Inference API) and where security fits into that flow.
Nice-to-Haves:
Experience with
AWS or GCP cloud security architecture.Experience using or securing Vector Databases or LLM orchestrators (like Lang Chain).
Previous experience in an embedded, automotive, or IoT environment (understanding that code eventually runs on a vehicle or physical hardware).
Salary Range for this role is $105,100 - $131,400 for California based applicants and $88,300 - $110,400 for Georgia based applicants.…
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