security
Posted Jun 9Senior Security Engineer, AI/ML
at Qualys
On-site
Responsibilities
- Design, train, and evaluate ML models from scratch, spanning both classical ML and deep learning, and develop end-to-end pipelines for ingestion, preprocessing, training, evaluation, and deployment.
- Implement and optimize RAG pipelines using embeddings and vector databases (e.g., FAISS, Pinecone, Qdrant ), with security and data-leakage controls built in from the start.
- Write robust backend APIs in Python to serve models, process data, and integrate with cloud infrastructure; monitor model performance, latency, and accuracy in production and iterate continuously.
- Design and execute offensive security assessments and red teaming campaigns against GenAI and ML-powered systems, including the agentic pipelines built in-house.
- Identify and classify novel threat vectors targeting model inference, training pipelines, and model-serving architectures.
- Lead & Communicate Collaborate cross-functionally with product and engineering teams to design secure AI-powered features and define hardening strategies.
- Develop proof-of-concepts, technical whitepapers, or blog posts on emerging threats and best practices; monitor threat intelligence and academic research on AI model security and supply chain risks.
Requirements
- Position Overview We are seeking a Senior Security Engineer – AI/ML who sits at the intersection of hands-on AI/ML engineering and offensive security research.
- You will both build and break: designing and deploying GenAI and agentic systems that power next-generation threat detection, while red teaming those same systems to uncover prompt injection exploits, adversarial inputs, model manipulation, and other emerging AI threats.
- You will set the technical bar for secure-by-design AI at Qualys, mentor other engineers, and translate research into production hardening strategies. Key
- Break Conduct in-depth research on security vulnerabilities in LLMs and AI systems, including prompt injection, jailbreaks, data leakage, model theft, and adversarial attacks.
- Represent Qualys in security and AI research communities through speaking, publishing, or standardization efforts, and mentor engineers on secure AI development. Required
- experience across software engineering / machine learning and security research, penetration testing, or exploit development, with a focus on application or cloud security.
- Strong programming skills in Python, including building APIs and backend components, plus scripting and automation for testing and PoC development.
- Experience training ML models using Scikit-learn, TensorFlow, or PyTorch , and a strong working knowledge of LLM architectures (transformers, embeddings, fine-tuning, RAG). Hands-on
- experience with LangChain , LlamaIndex , or other GenAI frameworks, and with building multi-agent or autonomous AI workflows.
- Familiarity with GenAI-specific risks such as prompt injection, model evasion, hallucination-based exploits, data leakage, or model theft, and with LLM deployment scenarios (e.g., OpenAI, HuggingFace , custom-hosted models) and their threat surfaces.
- Ability to analyze logs, API interactions, inference responses, and prompt chains to identify anomalous or risky behavior.
- Working knowledge of SQL, Pandas, and large-scale data processing, with
- experience developing and deploying ML systems in Agile environments.
- Strong analytical mindset, excellent technical writing skills, and familiarity with responsible disclosure practices, bug bounty programs, or security research ethics. Preferred
- Qualifications Background in AI/ML security red teaming or adversarial ML. Knowledge of vector database risks, insecure RAG pipelines, model fingerprinting, and AI model supply chain attacks.
- Experience using or contributing to tools such as AutoGen , CrewAI , MetaGPT , Guardrails.ai, LLM Guard, or Tracer.
- Familiarity with LLMs such as GPT-4, Claude, Mistral, LLaMA , or Falcon, and integrating them via APIs.
- Experience with cloud platforms (AWS, GCP, Azure), containerized deployments, and MLOps tooling for monitoring, retraining, and CI/CD automation.
- Familiarity with Secure SDLC and threat modeling frameworks (e.g., STRIDE, MITRE ATLAS) and AI-specific security checklists.
- Publications or presentations at conferences such as Black Hat, DEF CON, USENIX, NeurIPS , or OWASP, and contributions to AI/ML projects in security, compliance, or enterprise applications.
- Why Join Us? Leadership Impact: Help drive a security-first culture and shape the defense landscape of next-generation AI systems at global scale.
Experience
- Qualifications 6+ years of combined
Benefits
- Competitive Compensation: We offer a comprehensive
- benefits package, including healthcare, retirement plans, and more. *****************************************************************************************************************
- The salary range for this position is $115,000 - $140,000 per year.
- Final compensation will be determined based on several factors, including but not limited to skills, relevant experience, and work location.
- Please note this range reflects base salary and does not include incentive compensation or potential equity grants.
- benefits package. Qualys is an Equal Opportunity Employer, please see our EEO policy .
Additional details
- Come work at a place where innovation and teamwork come together to support the most exciting missions in the world! Company Overview Qualys is a leading provider of cloud-based security and compliance solutions, processing vast amounts of data to help our global customers secure their networks, devices, and applications.
- With a strong focus on innovation and scale, Qualys empowers organizations to achieve continuous security and compliance through real-time visibility and analytics.
- As we continue to grow, we are looking for passionate and skilled professionals to join our mission in redefining the future of cybersecurity.
- This is a senior, dual-mandate role for an engineer who is equally comfortable orchestrating multi-agent pipelines and RAG architectures as they are tearing them apart to find weaknesses.
- Contribute to and build internal tooling for scanning, fuzzing, and automating LLM vulnerability discovery.
- Our Work Environment Collaborative & Transparent: We use virtual collaboration and pairing tools to share ideas openly.
- Siloed work is discouraged — teamwork is our strength.
- Agile & Flexible: We focus on delivering incremental value, adapting processes only when they serve our goals.
- Diverse & Inclusive: We believe in building teams with diverse perspectives, which fuels creativity and innovative problem-solving.
- People-Focused: Our people are our most valuable asset .