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$48

How much do remote red cross gis jobs pay per hour?

As of Jul 13, 2026, the average hourly pay for remote red cross gis in the United States is $28.52, according to ZipRecruiter salary data. Most workers in this role earn between $21.63 and $33.65 per hour, depending on experience, location, and employer.

What is the difference between Remote Red Cross Gis vs Remote Red Cross Data Analyst?

AspectRemote Red Cross GisRemote Red Cross Data Analyst
Required CredentialsGIS certification, relevant degrees in geography or GISData analysis certification, degrees in data science, statistics, or related fields
Work EnvironmentFieldwork, mapping, spatial data management, often collaborativeData interpretation, reporting, and visualization, primarily computer-based
Employer & Industry UsageRed Cross, disaster response, humanitarian aidRed Cross, data-driven decision making, program evaluation

While both roles support Red Cross initiatives, Remote Red Cross Gis focuses on spatial data and mapping for disaster response, whereas Remote Red Cross Data Analyst emphasizes data analysis and reporting to inform strategies. Understanding these differences helps candidates choose the role that best matches their skills and career goals.

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Remote Red Cross Gis jobs near you
Infographic showing various Remote Red Cross Gis job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 86% Full Time, 11% Part Time, and 2% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $59,329 per year, or $28.5 per hour.
AI Safety Engineer (Red Teaming) - Remote

AI Safety Engineer (Red Teaming) - Remote

micro1 AI

Columbus, OH • Remote

$50 - $90/hr

Part-time

Posted 13 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