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About This Role
Santa Clara, California, United States of America IT Ref ID: JR\-017917
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!
We believe collaboration thrives in person. That’s why most of our teams work from the office full time, with flexibility when it’s needed. This model supports real\-time problem\-solving, stronger relationships, and the kind of precision that drives great outcomes.
Job Summary
Job Summary
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Palo Alto Networks is seeking a Director of Application Product Management to lead the strategic vision and transformation across key domains—including Entitlement \& Consumption Management, New Product Introduction, Licensing \& Provisioning, and Customer Support \& Success.
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In this high\-impact leadership role, you will define and execute a holistic product strategy spanning Salesforce, custom\-built applications, and the broader licensing and provisioning ecosystem. Your work will directly influence both our Product and Global Customer Service (GCS) organizations.
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Leveraging deep expertise in product licensing, entitlements, and consumption\-based models, you will translate business objectives into a clear and actionable Application Product Roadmap. Your solutions will enable scalable operations, enhance customer experience, and drive adoption across internal and external user journeys.
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Success in this role will require first\-principles thinking to break down complex problems and architect innovative, AI\-enabled solutions that streamline processes and accelerate value delivery.
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This role reports to the Senior Director of Application IT within the CIO organization. You will lead a team of high\-performing Application Product Managers and work in close partnership with executive leaders across all business functions to drive strategic initiatives, streamline operations, and deliver measurable business impact.
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This is a unique opportunity to shape the future of how we deliver customer value—championing innovation, driving operational excellence, and creating seamless, scalable experiences across the enterprise.
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Your Impact
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- Define and own the product vision, strategy, and roadmap for the full portfolio spanning Entitlement \& Consumption Management, New Product Introduction (NPI), Licensing \& Provisioning, and Customer Support \& Success that drives product activation, consumption measurement, reporting, case management and more. Ensure alignment with broader business objectives while identifying opportunities for competitive differentiation and transformational impact—especially in support of consumption\-based revenue models and enhanced pre\- and post\-sales experiences.
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- Build, mentor, and lead a high\-performing team of Application Product Managers, fostering a culture of innovation, accountability, and continuous improvement. Provide strategic direction, coaching, and career development to elevate individual and team performance.
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- Act as a trusted advisor to executive stakeholders across Product and GCS organizations. Leverage strong business acumen and influencing skills to shape strategic decisions and direction for customer experience (CX) operations through a combination of technology, data, iterative UI/UX design, rapid prototyping, and business process optimization. Translate complex technical concepts into clear business value to drive alignment on priorities.
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- Drive seamless integration of New Product Introductions (NPIs) into the post\-sales experience by partnering closely with Product, cross\-functional IT teams, and the Strategy organization to ensure a connected and scalable delivery model. Drive velocity improvements to significantly reduce time to market for new product introductions
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- Champion the adoption of advanced AI technologies across the CX portfolio, with a focus on embedding intelligent support capabilities directly into the in\-product experience, elevating customer engagement and issue resolution.
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- Collaborate with IT Architecture and Engineering leaders to ensure solutions are built with architectural integrity, scalability, security, and long\-term maintainability in mind—anticipating evolving needs in a consumption\-based business environment.
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- Oversee the entire product lifecycle across the CX, NPI, Licensing, and Provisioning portfolio—from ideation and discovery to development, launch, adoption, and continuous improvement. Establish strong governance and delivery frameworks to ensure timely and reliable execution, including initiatives that streamline quoting and provisioning processes.
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- Lead a data\-driven product management approach, defining success metrics and KPIs to guide decision\-making. Leverage analytics, market trends, and customer feedback to continuously improve product offerings and maximize business value.
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- Identify and drive transformation of complex business processes using modern technologies to enhance operational efficiency, reduce friction, and elevate both the customer and employee experience.
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Qualifications
Required Qualifications
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- 15\+ years of experience in IT Application Product Management, with deep domain expertise in Licensing, New Product Introduction (NPI), and post\-sales processes, demonstrating a strong record of strategic leadership and progressive responsibility.
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- 7\+ years in senior leadership roles, successfully overseeing teams of Product Managers and managing complex, strategic portfolios—preferably within high\-growth SaaS environments.
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- Demonstrated first\-principles thinking, with a history of delivering breakthrough solutions that challenge conventional industry norms and drive transformative outcomes.
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- Extensive expertise in Licensing, Entitlements, and Consumption models, with hands\-on experience in platforms such as Salesforce Service Cloud, Gainsight, and related GTM/CX technologies.
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- Advanced proficiency in AI\-driven solutions, including generative AI, intelligent automation, and cloud\-native AI/ML pipelines, with a proven track record of delivering tangible business value through innovative AI initiatives.
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- Strong grasp of AI tooling and infrastructure, and practical experience integrating AI capabilities into enterprise workflows.
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- Experience leading large, distributed global teams, driving cross\-functional collaboration, and fostering high\-performance cultures.
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- Proven success in optimizing IT product strategies for consumption\-based revenue models, with a focus on improving end\-to\-end sales processes and streamlining quoting and provisioning experiences.
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- Expertise in data architecture and analytics, including the ability to define measurement frameworks, derive insights from complex data sets, and guide data\-informed decision\-making.
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- Solid understanding of enterprise software security, compliance, and governance frameworks, particularly within regulated industries.
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- Bachelor’s degree in Computer Science, Business Administration, or a related field is required.
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- Bachelors degree or MBA or advanced degree preferred.
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- Change management certification or demonstrated success leading organizational transformation initiatives is highly desirable.
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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.
$223,000\.00 \- $306,500\.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?: Yes
Salary Context
This $223K-$306K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($264K) sits 23% above the category median. Disclosed range: $223K to $306K.
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.
Palo Alto Networks AI Hiring
Palo Alto Networks has 7 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Santa Clara, CA, US, Washington, DC, US, Seattle, WA, US. Compensation range: $225K - $308K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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
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