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Remote Automotive Functional Safety Engineer information

What is the difference between Remote Automotive Functional Safety Engineer vs Remote Automotive Systems Engineer?

AspectRemote Automotive Functional Safety EngineerRemote Automotive Systems Engineer
Required CertificationsISO 26262, ISTQB, safety-related certificationsSystems engineering certifications, UML, SysML
Work EnvironmentFocus on safety standards, safety analysis, and risk assessmentDesign, integration, and testing of automotive systems
Industry UsagePrimarily in safety-critical automotive componentsBroader automotive system development

The Remote Automotive Functional Safety Engineer specializes in safety standards and risk assessments for automotive systems, ensuring compliance with safety regulations like ISO 26262. In contrast, the Remote Automotive Systems Engineer focuses on designing and integrating various vehicle systems. Both roles require technical expertise but differ in their primary focus areas within automotive development.

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Remote Automotive Functional Safety Engineer jobs near you
Infographic showing various Remote Automotive Functional Safety Engineer job openings in the United States as of July 2026, with employment types broken down into 76% Full Time, 6% Part Time, and 18% Contract. Highlights an 100% Remote job distribution.
Trust & Safety Engineer (GenAI) - Remote

Trust & Safety Engineer (GenAI) - Remote

micro1 AI

Fort Worth, TX • Remote

$50 - $90/hr

Part-time

Posted 14 days ago

Job description

Role Title: AI Jailbreak & Prompt-Injection Security Expert


Role Type: Contractor


Location: Remote


micro1 is engaging AI Jailbreak & Prompt-Injection Security Experts to contribute to a cutting-edge customer initiative focused on AI safety and robustness. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Design and implement advanced methodologies for evaluating AI system safety, focusing on ethical jailbreaks, LLM red teaming, prompt injection, and tool-use abuse scenarios.
  2. Create comprehensive cross-domain elicitation strategies to uncover multi-turn and complex adversarial bypass patterns in AI models.
  3. Develop, maintain, and update regression test suites that systematically test for jailbreak susceptibility and prompt-injection vulnerabilities.
  4. Construct robust evaluation frameworks that stress-test AI models against real-world adversarial threats, aiming to enhance overall system robustness.
  5. Collaborate with technical stakeholders to translate security findings into actionable improvements for model safety and risk mitigation.
  6. Document methodologies, findings, and best practices in clear, well-structured written reports and presentations for both technical and non-technical audiences.


Preferred Qualifications

  1. 2+ years of expertise in adversarial machine learning, LLM red teaming, AI safety evaluation, or a closely related security domain
  2. Proven experience researching, testing, or uncovering vulnerabilities related to ethical jailbreaks, prompt injection, tool-use abuse, or adversarial AI attacks.
  3. Advanced degree (PhD, MS) in computer science, cybersecurity, machine learning, or a relevant discipline, or equivalent operational/professional background.
  4. High credibility and recognition within the AI security or adversarial ML community—such as published research, open-source tools, or conference presentations.
  5. Exceptional written and verbal communication skills, with a strong focus on clear documentation and collaborative problem-solving.
  6. Prior participation in multi-disciplinary projects or cross-functional AI safety initiatives is a plus.
  7. Familiarity with current LLM architectures, prompt engineering techniques, and security assessment tools is highly desirable.

Apply