Adversarial Machine Learning Engineer - Red Teaming

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.

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