Adversarial Machine Learning Engineer – Red Teaming

🔥 12 minutes ago

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C-Serv

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2013

💼 Consulting

📦 Logistics

📣 Marketing

Consulting • Logistics • Marketing

C-Serv is a tech recruitment and global workforce solutions provider that specializes in helping businesses find skilled talent. With a deep understanding of tech staffing challenges, C-Serv offers personalized recruitment strategies, rigorous vetting processes, and flexible placement options to meet the unique needs of high-growth organizations around the world. Their services extend to supporting clients with comprehensive workforce management, ensuring compliance with international regulations while connecting companies to the specialized talent they need to drive innovation and success.

📋 Description

• Conduct hands-on adversarial testing across models, applications, agentic layers, and data pipelines, including jailbreaks, guardrail bypasses, prompt injection, agent/tool-chain misuse, dangerous-capability evaluation, API abuse, data poisoning, model inversion, and membership inference. • Investigate edge-case findings from AI red-team campaigns and turn anomalies into understood, reproducible vulnerabilities. • Produce severity-ranked findings mapped to OWASP Top 10 for LLM Applications, the NIST AI Risk Management Framework and Generative AI Profile, MITRE ATLAS, and EU AI Act Article 55 expectations. • Provide evidence and clean reproduction steps. • Deliver actionable remediation guidance and perform retesting to confirm fixes. • Work closely with the client’s Guardrails and AI red-teaming team. • Translate technical findings into clear language for technical and non-technical stakeholders. • Stay engaged through remediation and validation.

🎯 Requirements

• Expert-level Python programming with deep proficiency in ML frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers • Hands-on experience fine-tuning ML models and Small Language Models (SLMs), including LoRA/QLoRA, PEFT, instruction tuning, and domain adaptation, for performance and robustness objectives • Strong foundation in ML mathematics: optimization, linear algebra, probability, and statistics • Proven ability to design and execute adversarial attacks, including evasion, data poisoning, model extraction, and membership inference • Experience implementing defenses such as adversarial training, robust fine-tuning, input sanitization, and differential privacy • Proficiency with Adversarial Robustness Toolbox (ART), CleverHans, and Foolbox • Experience red-teaming AI/LLM systems, including prompt injection, jailbreak testing, and safety/alignment evaluation • Ability to evaluate and benchmark model robustness, safety, and security posture before and after fine-tuning • Familiarity with MLOps practices, including model versioning, experiment tracking, and secure deployment pipelines • Strong threat-modeling skills and an attacker's mindset, with the ability to communicate risks clearly to technical and non-technical stakeholders • Active awareness of the latest adversarial ML and GenAI security research

🏖️ Benefits

• Fully remote working anywhere in the US, built around delivery rather than presence. • A clear path to grow into staff and principal-level technical influence. • Full support from C-Serv across the hiring process and beyond, with full-cycle accountability. • A values-led, woman-owned delivery partner built on empathy, integrity, collaboration, and growth.