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About This Role
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.
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.
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. You will set the technical bar for secure\-by\-design AI at Qualys, mentor other engineers, and translate research into production hardening strategies.
Key Responsibilities
*Build*
- Build and deploy GenAI applications using LangChain, LlamaIndex, or similar frameworks, and orchestrate agentic AI workflows with tools such as AutoGen, CrewAI, or custom agent\-based architectures.
- 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.
*Break*
- Conduct in\-depth research on security vulnerabilities in LLMs and AI systems, including prompt injection, jailbreaks, data leakage, model theft, and adversarial attacks.
- 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.
- Contribute to and build internal tooling for scanning, fuzzing, and automating LLM vulnerability discovery.
*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.
- Represent Qualys in security and AI research communities through speaking, publishing, or standardization efforts, and mentor engineers on secure AI development.
Required Qualifications
- 6\+ years of combined 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.
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. We invest in personal growth and align individual strengths to company objectives.
Why Join Us?
- Leadership Impact: Help drive a security\-first culture and shape the defense landscape of next\-generation AI systems at global scale.
- Cutting\-Edge Technology: Build and harden real\-world LLM, GenAI, and agentic systems on scalable, cloud\-native infrastructure.
- Professional Growth: Access broad resources, mentorship, certifications, and exposure to cutting\-edge research.
- Inclusive Culture: Join a team that values diverse thinking, critical research, openness, and continuous improvement.
- Competitive Compensation: We offer a comprehensive benefits package, including healthcare, retirement plans, and more.
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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. We also offer a comprehensive and highly competitive benefits package.
Qualys is an Equal Opportunity Employer, please see our EEO policy.
Salary Context
This $115K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 2064 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,963 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Qualys, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $180,000 based on 12,398 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($127K) sits 29% below the category median. Disclosed range: $115K to $140K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Qualys AI Hiring
Qualys has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Foster City, CA, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 15% (593 positions) offer remote work, while 3,349 require on-site attendance. Top AI hiring metros: New York (2,585 roles, $210,300 median); San Francisco (2,103 roles, $253,000 median); Los Angeles (1,764 roles, $190,500 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,963 open positions tracked in our dataset. By seniority: 116 entry-level, 1,875 mid-level, 1,532 senior, and 440 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (593 positions). The remaining 3,349 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,963 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,783), Data Scientist (297), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (116) are outnumbered by mid-level (1,875) and senior (1,532) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 440 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (593 positions), with 3,349 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $290,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,043 postings), Aws (1,241 postings), Azure (934 postings), Rag (886 postings), Gcp (774 postings), Pytorch (614 postings), Prompt Engineering (614 postings), Claude (564 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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