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Remote Audio Signal Processing Machine Learning Jobs in North Carolina

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Remote Audio Signal Processing Machine Learning information

What is the difference between Remote Audio Signal Processing Machine Learning vs Remote Audio Engineering?

AspectRemote Audio Signal Processing Machine LearningRemote Audio Engineering
Required CredentialsKnowledge of machine learning, signal processing, programming (Python, MATLAB)Audio engineering certifications, audio production experience
Work EnvironmentResearch labs, tech companies, remote collaborationRecording studios, broadcast companies, remote or onsite
Industry UsageDeveloping algorithms for audio enhancement, noise reduction, speech recognitionMixing, mastering, live sound, audio content creation

Remote Audio Signal Processing Machine Learning focuses on developing algorithms using machine learning techniques to improve audio quality and analysis. In contrast, Remote Audio Engineering involves practical audio production, mixing, and recording tasks. Both roles require audio knowledge, but the former emphasizes programming and data science, while the latter centers on sound quality and production skills.

What are popular job titles related to Remote Audio Signal Processing Machine Learning jobs in North Carolina? For Remote Audio Signal Processing Machine Learning jobs in North Carolina, the most frequently searched job titles are:
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Remote Audio Signal Processing Machine Learning jobs near you
AI Safety Engineer (Red Teaming) - Remote

AI Safety Engineer (Red Teaming) - Remote

micro1 AI

Charlotte, NC • 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.

Apply