Interested in this AI/ML Engineer role at Check Point Software Technologies?
Apply Now →About This Role
Company Description Why Join Us
At Check Point, what you do matters. Every day, we protect over 100,000 organizations worldwide from increasingly sophisticated cyber and AI\-driven threats, securing their AI transformation.
Our prevention\-first approach safeguards hybrid networks, cloud environments, digital workspaces, and AI systems, stopping attacks before they happen.
This is where innovation meets real\-world impact. You’ll help customers across industries operate with confidence in a rapidly changing digital world, working alongside smart, curious people who take ownership, challenge assumptions, and solve complex problems.
We’re proud to be recognized by TIME, Newsweek, and Forbes for excellence and workplace culture.
What really sets Check Point apart is the opportunity to grow, contribute, and help companies navigate their AI transformation securely.
If you’re excited to work at the forefront of AI\-driven security on a global scale, this is the place to do it.
Job Description
We're looking for an AI Security Engineer to join our Red Team and help us push the boundaries of AI security.
You'll run cutting\-edge security assessments, develop novel testing methodologies, and work directly with enterprise clients to secure their AI systems. This role combines hands\-on manual and automated red teaming, client engagement, and the development of automation that helps scale testing efforts. You'll thrive in this role if you want to be at the forefront of an emerging discipline, enjoy working on nascent problems, and like both breaking things and building processes that scale.
This is a highly cross\-functional position. AI security is still being defined, with best practices emerging in realtime. You'll be building the frameworks, methodologies, and tooling that scale our services while staying adaptable to rapid changes in the AI landscape. This role is ideal for someone who wants to take their traditional cybersecurity expertise and apply it to the new frontier of AI security and safety. Your focus will span several key areas:
Service Delivery \& Client Engagement
- Deliver AI red teaming security assessments for enterprise customers.
- Collaborate with clients to scope projects, define testing requirements, and establish success criteria
- Conduct comprehensive security assessments of AI systems, including text\-based LLM applications and multimodal agentic systems
- Extend testing to tools, APIs, and services these AI systems connect to
- Test manually and creatively beyond automated coverage to find the failures that tooling misses
- Author detailed security assessment reports with actionable findings and remediation recommendations
- Present findings and strategic recommendations to technical and executive stakeholders through report readouts and Q\&A sessions
Tooling \& Methodology Development
- Build upon and improve our established processes and playbooks to scale AI red teaming service delivery
- Develop frameworks to ensure consistent, high\-quality service delivery
- Find tedious, repetitive tasks and automate them. You don't need to be a world\-class developer—just someone who can build tools that make the team more effective. The goal is to automate routine work to free up time for targeted manual testing, not replace it.
Research \& Innovation
- Develop novel red teaming methodologies for emerging modalities: image, video, audio, autonomous systems
- Stay ahead of the latest AI security threats, attack vectors, and defense mechanisms
- Translate cutting\-edge academic and industry research into practical testing approaches
- Collaborate with our research and product teams to continuously level up our methodologies
Qualifications Technical Expertise
- Demonstrated offensive security experience shown through red teaming, penetration testing, security assessments, bug bounty programs, and/or CTF participation
- Demonstrated ability to find and exploit real vulnerabilities by hand, not only by running tools
- Strong knowledge of web application, API, and network security fundamentals
- Deep understanding of LLM vulnerabilities including direct and indirect prompt injection, jailbreaking, data poisoning, agentic and tool\-use risks
- Practical experience with threat modeling complex systems and architectures
- Proficiency in developing automated tooling to enable and enhance testing capabilities, improve workflows, and deliver deeper insights
Professional Skills
- Proven track record of contributing to or leading client\-facing security assessment projects from scoping through delivery
- Excellent technical writing skills with experience creating executive\-level security reports
- Strong presentation and communication skills, with the ability to clearly explain assessment findings and answer customer questions.
- Experience or interest building processes, documentation, and tooling for service delivery teams
AI Security Knowledge
- Understanding of AI/ML model architectures, training processes, and deployment patterns
- Familiarity with AI security frameworks such as the OWASP Top 10 for LLM Applications, the OWASP
- Top 10 for Agentic Applications, and MITRE ATLAS.
- Knowledge of emerging AI attack surfaces including multimodal systems and AI agents, and the tool and Model Context Protocol (MCP) integrations around them
Preferred Qualifications
- Relevant security certifications (OSCP, OSWA, OSAI, COAE, BSCP, etc.)
- Professional web application, API, or network penetration testing experience
- Hands\-on experience performing AI red teaming assessments, with a strong plus for experience targeting agentic systems
- Demonstrated experience designing LLM jailbreaks
- Active participation in security research and tooling communities
- Background in threat modeling and risk assessment frameworks
- Previous speaking experience at security conferences or industry events
What You'll Gain
- Opportunity to shape the future of AI security as an emerging discipline
- Work with cutting\-edge AI technologies and novel attack methodologies
- Lead high\-visibility projects with enterprise clients across diverse industries
- Collaborate with a world\-class research team pushing the boundaries of AI safety
- Research and development time between engagement cycles to contribute to team success
- Platform to establish thought leadership within the AI security community
Additional Information
All your information will be kept confidential according to EEO guidelines.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Check Point Software Technologies, 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Check Point Software Technologies AI Hiring
Check Point Software Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US.
Location Context
AI roles in Seattle pay a median of $228,700 across 516 tracked positions. That's 6% above the national 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.