Adversarial Machine Learning Engineer

C-Serv•Las Vegas, NV

About The Position

We are building a dedicated AI Red Team to rigorously test and harden enterprise-scale AI products. This role is for an adversarial machine learning specialist who thinks like an attacker, focusing on identifying vulnerabilities in LLM-driven systems, breaking model guardrails, exploiting data pathways, and stress-testing AI deployments before they reach enterprise customers. This is a hands-on technical role at the core of AI security.

Requirements

Strong experience in adversarial ML or AI security research
Experience working with LLM-based systems (OpenAI, Anthropic, open-source models, etc.)
Deep understanding of: Prompt injection techniques, Model jailbreak methodologies, AI system exploitation vectors
Strong Python skills
Experience building custom attack tooling or experimentation frameworks
Familiarity with: RAG architectures, Vector databases, Model fine-tuning workflows, API-based model deployments
Understanding of model safety mechanisms and guardrails

Nice To Haves

Background in cybersecurity or penetration testing
Familiarity with OWASP LLM Top 10
Experience working in enterprise environments

Responsibilities

Conduct adversarial testing across LLM and AI-based systems
Execute real-world attack simulations, including: Prompt injection, Jailbreaking and guardrail bypass, Data exfiltration attempts, Model inversion and evasion techniques, RAG manipulation
Develop scripts and tooling to automate attack scenarios
Analyse model behaviour under adversarial pressure
Identify systemic vulnerabilities in: APIs, Embedding pipelines, Vector databases, Fine-tuned model implementations
Collaborate with engineering teams to validate remediation
Document findings clearly and concisely
Help ensure AI systems are resilient before they are deployed at scale