C-Serv

Adversarial Machine Learning Engineer

🇨🇦 Vancouver, Канада Гібридно IT Старший спеціаліст Опубліковано Тра 19, 2026
Локація Vancouver, Канада
Формат роботи Гібридно
Рівень досвіду Старший спеціаліст
Категорія IT
IT-категорія Інженер з безпеки
Мова English
Опубліковано 19 травня 2026 р.
Остання перевірка 31 травня 2026 р.
Контекст JobGrid

Огляд ролі від JobGrid

Adversarial Machine Learning Engineer at C-Serv: Vancouver, Канада; Гібридно; Старший спеціаліст; IT; Інженер з безпеки. 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, Канада, Гібридно
  • Role classification: IT, Інженер з безпеки, Старший спеціаліст
  • 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.