C-Serv

Adversarial Machine Learning Engineer

🇨🇦 Vancouver, Canadá Híbrido Tecnología Senior Publicado May 19, 2026
Ubicación Vancouver, Canadá
Modalidad Híbrido
Seniority Senior
Categoría Tecnología
Categoría IT Ingeniería de seguridad
Idioma English
Publicado 19 de mayo de 2026
Última verificación 31 de mayo de 2026
Contexto de JobGrid

Resumen del puesto por JobGrid

Adversarial Machine Learning Engineer at C-Serv: Vancouver, Canadá; Híbrido; Senior; Tecnología; Ingeniería de seguridad. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.

  • Location and workplace: Vancouver, Canadá, Híbrido
  • Role classification: Tecnología, Ingeniería de seguridad, Senior
  • Source freshness: checked by JobGrid on 2026-05-31.
  • Application path: candidates continue to the employer application page with non-personal referral tags.

The Opportunity

 

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
  • 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

 

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