Interested in this AI/ML Engineer role at Palo Alto Networks?
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Texas, United States Sales Ref ID: JR\-016511
Our Mission
At Palo Alto Networks®, we’re united by a shared mission—to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real\-world problems with cutting\-edge technology and bold thinking. Here, everyone has a voice, and every idea counts. If you’re ready to do the most meaningful work of your career alongside people who are just as passionate as you are, you’re in the right place.
Who We Are
In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values: Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. If you are passionate about solving real\-world problems and ideating beside the best and the brightest, we invite you to join us!
This role is remote, but distance is no barrier to impact. Our hybrid teams collaborate across geographies to solve big problems, stay close to our customers, and grow together. You will be part of a culture that values trust, accountability, and shared success where your work truly matters.
Job Summary
The worldwide Learning \& Enablement team is dedicated to building a world\-class global sales organization. We are seeking a visionary learning architect to own the blueprint and execution that empowers our sellers to win in the age of AI\-driven security and sales transformation.
As the Sales Enablement Architect for AI, you are the strategist for how we prepare our sellers to succeed with our AI\-powered products, securing AI, and leveraging AI for their own productivity. You will be a skilled communicator who deeply understands the technical capabilities of our AI and security offerings and can translate them into compelling business and operational value. Your primary mission is to design the master plan for developing an elite sales team for this product area and equipping them with the knowledge, skills, and assets to win.
You will be at the center of a strategic ecosystem, partnering with sales leadership, technical experts, product management, and marketing to build and execute a cohesive and actionable enablement roadmap.
Key Responsibilities
- Architect and own an end\-to\-end global enablement strategy for AI, focusing on two key areas: our AI\-powered products to secure AI, and the enablement of our sellers to leverage AI for superior performance (e.g., hyper\-personalized outreach, AI meeting co\-pilots).
- Actively leverage AI to create dynamic, data\-driven learning paths that go beyond foundational knowledge, partnering with strategy teams to use signals from CRM and call intelligence to create adaptive learning journeys and reverse\-engineer success patterns into targeted, skill\-based learning interventions.
- Design, build, and personally deliver enablement initiatives, including live presentations, product launch readiness, and role\-based learning paths. This includes facilitating workshops, hosting training sessions, and developing on\-demand content.
- Establish and track key performance indicators for AI initiatives, rigorously measuring their impact on sales productivity, seller engagement, and business outcomes such as competitive win rates, sales cycle velocity, and new hire ramp time.
- Create an AI enablement roadmap for initiatives directly supporting key business objectives like quota attainment and lead conversion.
- Build and deploy practical GenAI solutions to address seller pain points and remove friction from daily workflows (e.g., a Sales Playbook Gem).
- Serve as the "voice of sales" by building trusted partnerships with key stakeholders across Sales, Product, and GTM teams, ensuring all enablement solutions are relevant, timely, and aligned to driving key business outcomes.
- Influence and guide the creation of high\-impact sales assets by providing strategic content plans and subject matter expertise to content development teams. Ensure resources like playbooks, battle cards, and pitch decks simplify complexities and are continuously updated.
Qualifications
- 8\+ years of combined professional experience within enterprise software or cybersecurity environments, including a minimum of 4\+ years of dedicated, hands\-on Sales Enablement experience owning seller readiness, content delivery, and learning programs.
- Strong cross functional leadership skills with the ability to influence Product, Marketing, Sales leaders without direct authority.
- Proven experience creating and deploying custom GenAI prompts, agents, or internal tools to optimize business or learning workflows.
- Experience leveraging AI tools and data insights (CRM, call intelligence, learning analytics, etc.) to create scalable, personalized enablement programs.
- Demonstrated ability to build role based learning paths, launch readiness programs, and sales assets that drive adoption and field success.
- Exceptional presentation and facilitation skills, with experience delivering training to global sales organizations and executive audiences.
- Experience defining success metrics and using data to measure enablement effectiveness, including seller adoption, productivity, quota attainment, and time to productivity.
- Bachelor's degree or equivalent experience; advanced degree or relevant AI, enablement, or sales certifications are a plus.
Compensation Disclosure
The compensation offered for this position will depend on qualifications, experience, and work location. For candidates who receive an offer at the posted level, the starting base salary (for non\-sales roles) or base salary \+ commission target (for sales/com\-missioned roles) is expected to be the annual range listed below. The offered compensation may also include restricted stock units and a bonus.
$148,000\.00 \- $238,500\.00/yr
Our Commitment
We’re trailblazers that dream big, take risks, and challenge cybersecurity’s status quo. It’s simple: we can’t accomplish our mission without diverse teams innovating, together.
We are committed to providing reasonable accommodations for all qualified individuals with a disability. If you require assistance or accommodation due to a disability or special need, please contact us at [email protected].
Palo Alto Networks is an equal opportunity employer. We celebrate diversity in our workplace, and all qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or other legally protected characteristics.
All your information will be kept confidential according to EEO guidelines.
Is role eligible for Immigration Sponsorship? No. Please note that we will not sponsor applicants for work visas for this position.
Salary Context
This $148K-$238K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Palo Alto Networks, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($193K) sits 12% below the category median. Disclosed range: $148K to $238K.
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.
Palo Alto Networks AI Hiring
Palo Alto Networks has 9 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Santa Clara, CA, US, Burbank, CA, US. Compensation range: $235K - $357K.
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
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