Vulnerability Management Analyst
Alignerr · New York, NY
- Hybrid
- Contract
- $75,000 / year
- New York, NY
Job highlights
- Analyze vulnerabilities and security scenarios for AI training.
- Classify severity and impact using industry frameworks.
- Evaluate and validate remediation strategies.
- Generate and label security data for AI models.
- Work remotely on a flexible schedule.
About the role
Vulnerability Management Analyst (AI Training)
Alignerr is partnering with the world's leading AI research labs to build smarter, more capable AI systems. We are seeking experienced security practitioners to make this happen. As a Vulnerability Management Analyst, you will leverage your real-world knowledge of CVEs, exposure management, and remediation workflows to help train and evaluate cutting-edge AI models. This is a unique opportunity to contribute to meaningful work at the intersection of cybersecurity and artificial intelligence, with the flexibility to work on your own schedule, from anywhere in the world.
Responsibilities
- Analyze vulnerability reports, CVEs, and exposure scenarios across infrastructure and application environments.
- Classify severity, exploitability, and business impact using industry-standard frameworks.
- Evaluate and validate patching, mitigation, and remediation decision-making scenarios.
- Generate, label, and review realistic security-reasoning data for training and benchmarking AI systems.
- Apply practitioner instincts to help AI understand the difference between theoretical risk and actual production impact.
Qualifications
- 2+ years of experience in vulnerability management, security operations, or infrastructure security.
- Solid working knowledge of CVEs, vulnerability scanners, patching workflows, and risk prioritization frameworks (e.g., CVSS, EPSS, SSVC).
- Understanding of real-world tradeoffs security teams face in managing and prioritizing risk at scale.
- Analytical and structured thinker capable of communicating complex security concepts clearly.
- Self-motivated and comfortable working independently on asynchronous, task-based assignments.
Nice to Have
- Experience with vulnerability management platforms such as Tenable, Qualys, Rapid7, or similar.
- Familiarity with cloud infrastructure security or container environments.
- Background in threat intelligence, red teaming, or security engineering.
- Prior experience contributing to AI training, data labeling, or technical evaluation projects.
Why Join Us
- Work directly on frontier AI systems alongside top research labs.
- Fully remote and flexible work schedule.
- Enjoy freelance perks: autonomy, variety, and global collaboration.
- Apply deep security expertise to a high-impact, emerging field.
- Potential for ongoing work and contract extensions.
Key skills/competency
- Vulnerability Management
- Cybersecurity
- AI Training
- CVE Analysis
- Risk Prioritization
- Remediation Workflows
- Security Operations
- Infrastructure Security
- Data Labeling
- AI Models
Skills & topics
- Vulnerability Management
- Cybersecurity
- AI Training
- CVE Analysis
- Risk Prioritization
- Remediation
- Security Operations
- Remote Work
- Contract
- AI
How to get hired
- Tailor your resume: Highlight your 2+ years in vulnerability management, CVE knowledge, and risk prioritization frameworks.
- Showcase AI/ML familiarity: Emphasize any experience with AI training, data labeling, or technical evaluation projects.
- Demonstrate independent work: Provide examples of self-motivation and successful asynchronous task completion.
- Understand the role: Be ready to discuss how your security expertise applies to AI model training.
Technical preparation
Master CVEs and risk prioritization frameworks.,Practice analyzing vulnerability reports.,Familiarize with remediation workflows.,Understand CVSS, EPSS, SSVC scoring.
Behavioral questions
Describe a complex security concept you explained.,How do you prioritize risks independently?,Share an example of self-motivation.,Discuss a real-world security tradeoff you managed.
Frequently asked questions
- How does the Vulnerability Management Analyst role at Alignerr differ from traditional security roles?
- This Vulnerability Management Analyst role at Alignerr focuses on applying your cybersecurity expertise to train and evaluate AI models. You'll analyze vulnerability data and remediation scenarios to create realistic security-reasoning datasets, a unique application of traditional skills in the AI training domain.
- What kind of AI models will I be training as a Vulnerability Management Analyst at Alignerr?
- As a Vulnerability Management Analyst at Alignerr, you will be contributing to the training and evaluation of cutting-edge AI models designed to understand and reason about cybersecurity threats and vulnerabilities. The goal is to enhance their ability to identify, assess, and suggest remediation for real-world security risks.
- Is the Vulnerability Management Analyst position at Alignerr remote?
- Yes, the Vulnerability Management Analyst position at Alignerr is fully remote. You can work from anywhere in the world, offering flexibility in your work location and schedule.
- What are the expected working hours for a Vulnerability Management Analyst at Alignerr?
- The commitment for the Vulnerability Management Analyst role at Alignerr is between 10-40 hours per week. This allows for flexibility, enabling you to work on your own schedule as part of this hourly contract position.
- What specific cybersecurity frameworks are important for the Vulnerability Management Analyst role at Alignerr?
- For the Vulnerability Management Analyst role at Alignerr, a solid working knowledge of CVEs and risk prioritization frameworks such as CVSS, EPSS, and SSVC is important. Understanding these will help in classifying severity, exploitability, and business impact accurately.
- How does Alignerr leverage the skills of a Vulnerability Management Analyst for AI training?
- Alignerr leverages the skills of a Vulnerability Management Analyst by having them analyze real-world vulnerability data, classify risks, and generate labeled security-reasoning data. This practical input is crucial for training and benchmarking AI models to better understand production security matters.