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Llm Engineer Remote Jobs Near Me

Join a National Top Workplace Named a Top Workplace in the USA and Top Remote Workplace, Kobie is ... Build evaluation harnesses (golden datasets, LLM-as-judge, regression suites) using AgentCore ...

Join a National Top Workplace Named a Top Workplace in the USA and Top Remote Workplace, Kobie is ... Build evaluation harnesses (golden datasets, LLM-as-judge, regression suites) using AgentCore ...

Senior Staff Agentic AI Engineer

Columbus, OH · On-site +1

$102K - $139K/yr

... g., LLM-powered applications, LangChain, LangGraph, AutoGen) and 3+ years of experience ... Normal office environment. (Remote or Hybrid), 3 to 4 days per month are required in office if ...

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Llm Engineer Remote information

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How much do llm engineer remote jobs pay per hour?

As of Aug 13, 2026, the average hourly pay for llm engineer remote in the United States is $53.63, according to ZipRecruiter salary data. Most workers in this role earn between $43.27 and $62.26 per hour, depending on experience, location, and employer.
What cities are hiring for Llm Engineer Remote jobs? Cities with the most Llm Engineer Remote job openings:
What states have the most Llm Engineer Remote jobs? States with the most job openings for Llm Engineer Remote jobs include:
What are the most commonly searched types of Llm Engineer jobs? The most popular types of Llm Engineer jobs are:
A map of the United States highlighting the number of Llm Engineer Remote job openings by state according to ZipRecruiter. The image is accompanied by a detailed chart listing the number of Llm Engineer Remote job openings in each state, with California having the most at 2 and Alaska the least at 0.

Trust & Safety Engineer (GenAI) - Remote

micro1 AI

Columbus, OH • Remote

$50 - $90/hr

Part-time

This job post has expired today. Applications are no longer accepted.


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.