Interested in this AI/ML Engineer role at Check Point Software Technologies?
Apply Now →Skills & Technologies
About This Role
Company Description
As the world’s leading vendor of Cyber Security, facing the most sophisticated threats and attacks, we’ve assembled a global team of the most driven, creative, and innovative people. At Check Point, our employees are redefining the security landscape by meeting our customers’ real\-time needs and providing our cutting\-edge technologies and services to an ever\-growing customer base.
Check Point Software Technologies has been honored by Time Magazine as one of the World’s Best Companies and Newsweek’s list of Americas Best Cybersecurity Companies. We've also earned a spot on the Forbes list of the World’s Best Places to Work for five consecutive years and recognized as one of the World’s Top Female\-Friendly Companies. If you're passionate about making the world a safer place and want to be part of an award\-winning company culture, we invite you to join us.
We are seeking a hands\-on Senior Pre\-Sales Solutions Architect to join our Americas Solution Architecture team, with a primary focus on leading proofs of value (POVs) and proofs of concept (POCs) for next\-generation firewall (NGFW), hyperscale security, and AI security solutions. Based in the Dallas–Fort Worth metro and supporting customers across the Americas, this role pairs deep technical expertise with strong customer\-facing skills to drive high\-impact pre\-sales engagements that convert to design wins and revenue.
The ideal candidate brings deep operational expertise with Check Point Maestro hyperscale orchestration, multi\-vendor NGFW exposure, modern data center fabric design, SASE/SD\-WAN edge architectures, and advanced threat prevention. They should also be conversant in DPU\-accelerated security on Nvidia BlueField at 100/400 GbE line rates, and bring working knowledge of AI\-SPM, LLM/GenAI security, RAG pipeline protection, MCP\-based agentic workflows, and AI data center and Industry 4\.0 / smart factory reference architectures.
Job Description
- Own the end\-to\-end POV / POC lifecycle across the Americas — qualification, scoping, success criteria definition, lab and on\-site build, test plan execution, results analysis, executive readouts, and handoff to post\-sales.
- Partner with account teams (Account Executives, SEs, Channel Managers) as the lead technical authority on NGFW, Maestro, and AI security opportunities; translate customer business requirements into winning technical solutions.
- Architect, demonstrate, and validate NGFW platforms across on\-prem, colo, hybrid cloud, and AI factory environments, with a focus on high availability, elastic scale, low\-latency throughput, and Zero Trust segmentation.
- Design and demonstrate Check Point Maestro hyperscale deployments, including multi\-stack scaling, multi\-MHO orchestration, security group design, and seamless inter\-site / inter\-DC interconnect.
- Engineer reference designs and POC topologies for the underlying data center fabric required to support Maestro orchestration, micro\-segmentation, and east/west inspection at scale across leaf/spine and EVPN\-VXLAN topologies.
- Demonstrate and validate Nvidia BlueField DPU integrations at 100 GbE (and emerging 400 GbE) for accelerated security services, including DOCA familiarity, host onboarding, and performance benchmarking.
- Architect and showcase security for AI factories and AI\-ready data centers, including GPU cluster east/west protection, secure multi\-tenant model training and inference fabrics, and high\-throughput inspection points across RoCEv2 / InfiniBand\-adjacent networks.
- Lead AI security POVs across the GenAI stack — securing RAG pipelines (vector DB access, embedding integrity, data poisoning, prompt\-injection defense), MCP servers and agentic tool\-use workflows, model and inference endpoints, and shadow\-AI discovery — aligned to OWASP Top 10 for LLMs, MITRE ATLAS, and NIST AI RMF.
- Position and demonstrate AI\-SPM (AI Security Posture Management), DLP, and CASB\-style controls to govern enterprise GenAI usage and protect sensitive data in transit to public and private LLMs.
- Architect and demonstrate integrated SASE / SD\-WAN solutions with data center security, delivering unified policy across user\-to\-app, branch\-to\-DC, and DC\-to\-cloud traffic flows.
- Develop and deliver technical enablement — instructor\-led training, hands\-on labs, design workshops, lunch\-and\-learns, and reference materials — for customers, partners, and internal field teams across the Americas.
- Lead competitive evaluations, bake\-offs, and migrations between Check Point and other NGFW platforms (Palo Alto, Fortinet, Cisco, Juniper, Zscaler, etc.); produce HLDs, LLDs, BoMs, design guides, and operational runbooks.
- Represent the company at customer briefings, executive business reviews (EBRs), partner events, regional trade shows, and industry conferences across the Americas.
- Provide structured field feedback to Product Management and Engineering on customer requirements, competitive gaps, and emerging use cases — particularly in AI security and AI infrastructure.
Qualifications
- 8\+ years in network security, with significant time in a customer\-facing pre\-sales, solutions architect, or sales engineering role.
- Demonstrated track record owning and winning NGFW and Maestro POVs / POCs end\-to\-end, with strong stakeholder management through to technical decision and commercial close.
- Extensive hands\-on NGFW experience in enterprise data center and hybrid cloud environments — not branch / campus only.
- Strong production experience with Check Point Maestro hyperscale architectures, including multi\-MHO topologies, security group design, and inter\-stack interconnect.
- Hands\-on experience with at least one additional Tier\-1 NGFW platform at comparable scale (Palo Alto PAN\-OS / Strata, Fortinet FortiGate, Cisco Secure Firewall / Firepower, Juniper SRX, or equivalent).
- Working knowledge of SASE / SSE / SD\-WAN architectures and modern threat prevention services — IPS, anti\-bot, anti\-malware, sandboxing/threat emulation, URL filtering, TLS/SSL inspection, DNS security, and CDR.
- Demonstrated understanding of AI security across the GenAI lifecycle, including:
- + RAG pipeline security (vector store hardening, embedding leakage, data poisoning, retrieval injection)
+ MCP (Model Context Protocol) server exposure and agentic tool\-use risk
+ LLM application security aligned to OWASP Top 10 for LLMs, MITRE ATLAS, and NIST AI RMF
+ AI\-SPM, model supply chain risk, and shadow\-AI / GenAI usage governance
- Familiarity with AI data center / AI factory concepts: GPU cluster networking, RoCEv2 / InfiniBand awareness, Nvidia Spectrum\-X and DGX reference architectures, and secure east/west insertion for training and inference workloads.
- Exposure to AI smart factory / Industry 4\.0 environments, including IT/OT convergence, IEC 62443\-aligned segmentation, industrial protocol awareness (Modbus, OPC UA, Profinet, EtherNet/IP), and securing AI\-driven automation, computer vision, and predictive maintenance workloads.
- Proven experience designing and delivering technical enablement (ILT, VILT, hands\-on labs, workshops) to customers and partners.
- Deep data center switching expertise: L2/L3, VLAN/VXLAN, EVPN, BGP, LACP/MLAG, and the design considerations for inserting firewall clusters into modern leaf/spine fabrics.
- Hands\-on experience with Nvidia BlueField DPUs (or comparable SmartNIC / IPU platforms) at 100 GbE, including provisioning, offload configuration, DOCA familiarity, and performance troubleshooting.
- Excellent written, verbal, and whiteboarding skills; able to present credibly to network/security engineers, enterprise architects, and C\-level stakeholders (CISO, CIO, CTO).
- Comfortable traveling 20–30% across the Americas, including occasional LATAM travel; valid passport required.
- Spanish or Portuguese language skills are a plus given LATAM coverage.
Nice to Have
- CCIE / CCNP, CCSE / CCSM / CCTE, PCNSE, NSE 7\+, or equivalent industry certifications.
- Check Point Certified Trainer (CCSI) or equivalent formal training credential.
- Prior experience in a vendor or VAR pre\-sales SA / SE organization with regional or theater\-level coverage.
- Hands\-on experience with GenAI security platforms (Check Point GenAI Protect, Palo Alto AI Runtime Security, Protect AI, Prompt Security, Lakera, Robust Intelligence) and guardrail frameworks (NeMo Guardrails, Llama Guard, Guardrails AI).
- Exposure to Nvidia AI reference architectures (DGX SuperPOD, Spectrum\-X, BlueField DOCA) and AI factory / smart manufacturing deployments.
- Automation and Infrastructure\-as\-Code experience (Ansible, Terraform, Python) for firewall policy, fabric, and security service orchestration.
- Familiarity with cloud\-native security (CNAPP, CWPP, CSPM) and container/Kubernetes security in AI/ML pipelines.
- Experience with DPU\-accelerated security and networking use cases (DOCA, OVS offload, line\-rate telemetry, in\-line threat inspection).
Location \& Travel Requirement Candidate must be based in the Dallas / DFW metro area — this is an on\-site role at our local hub when not traveling.
Expect 20–30% travel across the Americas (US, Canada, and LATAM) supporting POVs, customer briefings, partner enablement, and field events. No relocation assistance, no fully remote.
EOE M/F/Veterans/Disabled
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 3,708 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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Check Point Software Technologies AI Hiring
Check Point Software Technologies has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Atlanta, GA, US, San Diego, CA, US, Irving, TX, US. Compensation range: $165K - $165K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.