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Mobile Remote Red Cross Jobs (NOW HIRING)

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Mobile Remote Red Cross information

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$41K

$136.6K

$183.5K

How much do mobile remote red cross jobs pay per year?

As of Jul 11, 2026, the average yearly pay for mobile remote red cross in the United States is $136,616.00, according to ZipRecruiter salary data. Most workers in this role earn between $119,500.00 and $159,000.00 per year, depending on experience, location, and employer.

What is the difference between Mobile Remote Red Cross vs Mobile Blood Collection Technician?

AspectMobile Remote Red CrossMobile Blood Collection Technician
CertificationsCPR, First Aid, Bloodborne PathogensCPR, First Aid, Bloodborne Pathogens
Work EnvironmentRemote, community outreach, mobile unitsMobile blood drives, community settings
Employer & IndustryAmerican Red Cross, nonprofit healthcareBlood banks, hospitals, nonprofit healthcare

Both roles require similar certifications and work in community or mobile settings. The Mobile Remote Red Cross focuses on outreach and coordination remotely, while the Mobile Blood Collection Technician directly collects blood donations at mobile sites. Understanding these differences helps candidates choose the role that best fits their skills and career goals.

What are the most commonly searched types of Remote Red Cross jobs? The most popular types of Remote Red Cross jobs are:
What job categories do people searching Mobile Remote Red Cross jobs look for? The top searched job categories for Mobile Remote Red Cross jobs are:
AI Safety Engineer (Red Teaming) - Remote

AI Safety Engineer (Red Teaming) - Remote

micro1 AI

Denver, CO • Remote

$50 - $90/hr

Part-time

Posted 12 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.