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Adversarial Machine Learning Engineer

Job in Portland, Multnomah County, Oregon, 97204, USA
Listing for: C-Serv
Full Time position
Listed on 2026-05-29
Job specializations:
  • IT/Tech
    AI Engineer, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 125000 - 150000 USD Yearly USD 125000.00 150000.00 YEAR
Job Description & How to Apply Below

We are building a dedicated AI Red Team to rigorously test and harden enterprise-scale AI products.

We are looking for an adversarial machine learning specialist who thinks like an attacker.

This role focuses 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.

What You’ll Do
  • Conduct adversarial testing across LLM and AI-based systems
  • Execute real-world attack simulations, including prompt injection, jailbreak, guardrail bypass, data exfiltration attempts, model inversion and evasion techniques, and RAG manipulation
  • Develop scripts and tooling to automate attack scenarios
  • Analyze model behaviour under adversarial pressure
  • Identify systemic vulnerabilities in APIs, embedding pipelines, vector databases, and fine‑tuned model implementations
  • Collaborate with engineering teams to validate remediation
  • Document findings clearly and concisely

You will help ensure AI systems are resilient before they are deployed at scale.

Requirements Core Technical Skills
  • 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, and AI system exploitation vectors
  • Strong Python skills

    Experience building custom attack tooling or experimentation frameworks
AI Systems Knowledge
  • Familiarity with RAG architectures, vector databases, model fine‑tuning workflows, API-based model deployments, and understanding of model safety mechanisms and guardrails
Nice to Have
  • Background in cybersecurity or penetration testing
  • Familiarity with OWASP LLM Top 10
  • Experience working in enterprise environments
Who You Are
  • Curious and relentless
  • Comfortable thinking like an attacker
  • Creative in finding non‑obvious vulnerabilities
  • Detail‑oriented but fast‑moving
  • Comfortable operating in ambiguity
  • Independent but collaborative
Benefits
  • Comprehensive Private Medical Coverage
  • Support for Mental Health Expenses
  • Life Insurance Options
  • Attractive Compensation Package
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