Binance Accelerator Program – AI Infra Security Engineer

AI-Powered Penetration Testing:

Use LLM-driven agents to automate reconnaissance, attack path discovery, proof-of-concept (PoC) generation, and false-positive triage across cloud infrastructure, internal applications, and APIs.

Benchmark AI-driven findings against human-led assessments, measure detection accuracy and coverage, and refine capabilities based on validated real-world results.

AI-Driven Infrastructure Security:

Use AI to identify critical CVEs affecting Binance's systems and generate actionable remediation recommendations.

Use AI to detect system misconfigurations, flag potential security risks, and trigger alerts.

Automate log analysis across diverse systems using AI to identify suspicious activity, detect potential threats, and generate actionable alerts.

AI Systems Security:

Use AI to identify security vulnerabilities in LLM applications, AI agents, and MCP servers, including prompt injection, jailbreaks, tool misuse, data exfiltration, and privilege escalation.

Translate red-team findings into actionable security controls, such as least-privilege IAM and tool permissions, sandboxing, input/output filtering, human-in-the-loop approvals, and comprehensive audit logging.

Integrate AI security regression testing into CI pipelines to continuously validate new models, prompts, and tools against emerging attack scenarios before production deployment.

Majoring in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, or a closely related field.

Knowledge of at least one cloud platform (AWS, GCP, or Azure), with personal experience deploying something on it.

Hands-on experience using LLMs and coding agents in projects, applied to at least one security task (e.g., code review, recon, or vulnerability analysis).

Hands-on experience in AI/ML, or with prompt injection or jailbreaks against LLM applications, gained through internships, work experience, research projects, or competitions.

Strong interest in AI and solid understanding of modern AI technologies such as LLMs, agents, and deep learning, including their strengths and limitations; practical experience applying AI to real problems and an AI-native approach to daily work.

Strong problem-solving skills, analytical rigor, and the ability to work on ambiguous, adversarial problems where attackers continually adapt.

Strong communication and documentation skills, with the ability to explain technical findings clearly to both technical and non-technical stakeholders.

Able to commit to an internship of at least 6 months preferred, with consistent weekly hours.