Accountabilities:

• Conduct hands-on adversarial testing across AI models, applications, agentic systems, and data pipelines to identify security vulnerabilities and weaknesses.
• Perform advanced red-team assessments including multi-turn jailbreaks, guardrail bypass testing, prompt injection analysis, agent and tool-chain misuse evaluation, and dangerous capability assessments.
• Investigate edge-case findings from AI security campaigns and transform anomalies into fully understood, reproducible vulnerabilities.
• Assess risks related to data poisoning, model inversion, membership inference, model extraction, and other adversarial machine learning threats.
• Develop and execute security evaluations aligned with industry frameworks such as OWASP Top 10 for LLM Applications, NIST AI Risk Management Framework, MITRE ATLAS, and relevant AI regulations.
• Produce detailed vulnerability reports with severity ratings, evidence, reproduction steps, and actionable remediation recommendations.
• Partner closely with AI safety, engineering, and security teams throughout vulnerability discovery, remediation, and validation processes.
• Retest implemented fixes to confirm that security improvements are effective and vulnerabilities have been properly addressed.
• Communicate technical findings clearly to both engineering teams and non-technical stakeholders by translating complex AI risks into understandable business impacts.
• Stay current with emerging adversarial machine learning techniques, generative AI security research, and evolving threat landscapes.
• Contribute to improving AI security methodologies, testing practices, and defensive strategies.

Requirements:

• Expert-level Python programming skills with strong experience using machine learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers.
• Hands-on experience fine-tuning machine learning models and Small Language Models (SLMs), including techniques such as LoRA, QLoRA, PEFT, instruction tuning, and domain adaptation.
• Strong foundation in machine learning mathematics, including optimization, linear algebra, probability, and statistics.
• Proven experience designing and executing adversarial machine learning attacks, including adversarial examples, data poisoning, model extraction, and membership inference.
• Experience implementing AI security defenses such as adversarial training, robust fine-tuning, input sanitization, and differential privacy.
• Familiarity with adversarial machine learning frameworks such as Adversarial Robustness Toolbox, CleverHans, and Foolbox.
• Experience conducting AI and LLM red-team exercises, including jailbreak testing, prompt injection assessments, and safety evaluation.
• Ability to evaluate and benchmark AI model robustness, security posture, and safety before and after fine-tuning.
• Experience with MLOps practices, including model versioning, experiment tracking, and secure model deployment pipelines.
• Strong threat-modeling skills with the ability to think like an attacker while communicating risks effectively.
• Ability to collaborate with technical and non-technical stakeholders in a fast-moving environment.
• Active knowledge of emerging adversarial ML research, generative AI security trends, and industry best practices.

Benefits:

• Fully remote position available anywhere in Canada.
• Flexible work environment focused on outcomes, delivery, and technical impact.
• Opportunity to work on advanced AI security challenges involving foundation models and emerging technologies.
• Clear career growth path toward staff and principal-level technical influence.
• Supportive environment with dedicated guidance throughout the hiring process and beyond.
• Opportunity to collaborate with experienced AI security professionals and contribute to meaningful security initiatives.
• Values-driven culture focused on empathy, integrity, collaboration, and continuous growth.