Interested in this AI/ML Engineer role at Palo Alto Networks?
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Santa Clara, California, United States Sales Ref ID: JR\-018933
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
As a Senior Consulting Director within Unit 42’s Cyber Risk Management team, you will lead our AI Security Assessment offering and own the delivery success of the service line in North America. This includes overseeing a high\-performing team of consultants delivering these critical capabilities, maintaining delivery quality, driving revenue and utilization, supporting business development, and advancing service innovation. Working closely with the Managing Director and CRM NAM Practice leader, along with peers in North America and other theaters and regions, you will provide executive oversight and strategic direction for how we evaluate, mature, and optimize security operations through our assessments across our client base — driving greater security outcomes, product integration, and transformation at scale. In this strategic leadership role, you will serve as the executive sponsor on key engagements, guide a high\-performing team of Directors and consultants, and partner across Unit 42 and Palo Alto Networks to refine services, contribute to thought leadership, and elevate AI Security capabilities across industries. You will be responsible for setting and achieving service line KPIs, including delivery excellence, customer satisfaction, and introducing Palo Alto Networks’ products as solutions to risks identified during engagements. This role requires deep expertise in AL/ML Security and consulting, experience managing delivery teams, and the ability to influence both internal and external stakeholders. You will work cross\-functionally with sales, product, service innovation and marketing teams to evolve and scale Unit 42’s AI Security capabilities, integrating best practices and Palo Alto Networks product offerings.
Key Responsibilities
- Provide strategic leadership and vision for Unit 42’s global AI Security Assessment services, ensuring alignment with organizational goals, customer needs, and emerging threat trends
- Own and drive success across all AI Security Assessment service line KPIs, including revenue growth, delivery excellence, utilization, customer satisfaction, and Security Transformation Program (STP) contribution
- Lead the continual maturation and innovation of AI Security Assessment methodologies, deliverables, and tooling by integrating industry best practices, threat intelligence, and customer feedback
- Serve as the executive sponsor, strategic advisor and delivery lead for complex, high\-impact AI Security engagements, ensuring delivery quality, client satisfaction, and long\-term value realization
- Deliver strategic guidance to CISOs, CTOs, and executive stakeholders on optimal security for generative AI technology implementation, agentic and CI/CD pipeline security and integrity, and AI governance and policies for safe AI usage and development standards
- Embed Palo Alto Networks technologies such as Cortex PrismaAIRS, Koi Agentic Security, XDR, XSIAM, and XSOAR into assessment and transformation engagements to drive measurable security outcomes
- Own delivery staffing and capacity planning to ensure the right mix of talent, availability, and expertise across concurrent engagements
- Lead and mentor a high\-performing team of Directors and consultants, fostering career growth, technical depth, and leadership capability
- Collaborate with GTM teams to shape messaging, identify new opportunities, and support the successful closing of AI Security Assessment engagements through solutioning and executive engagement
- Lead and contribute to complex scoping and quoting efforts, ensuring accurate effort estimates, appropriate resourcing, and alignment to customer goals
- Build and nurture trusted advisor relationships with key customers, especially Unit 42 retainer accounts, to drive repeat business and long\-term strategic value
- Represent Unit 42 as a AI Security subject matter expert at key industry events, roundtables, webinars, and forums to enhance thought leadership and market credibility
- Author and contribute to whitepapers, blogs, and internal enablement materials to advance the team’s knowledge and influence in the AI/ML Security domain
- Champion a culture of excellence, innovation, and collaboration, fostering a positive and inclusive environment that attracts, retains, and develops top talent
- Act as the internal voice of the AI Security Assessment service line, collaborating across product, sales, and delivery teams to influence service roadmap, product alignment, and market positioning
- Continuously monitor the evolving security operations landscape, including adversary techniques, AI Security tooling trends, and customer challenges, to evolve service offerings and maintain relevance
Qualifications
Required Qualifications
- 10\+ years of experience in cybersecurity, with 5\+ years leading AI/ML\-related services or operations (e.g. AI / agentic development, AI consulting, etc)
- 7\+ years managing consulting delivery teams or security operations teams, ideally at the Director level or higher
- Deep understanding of AI/ML implementations and security
- Experience conducting SOC assessments, maturity benchmarking, or blue team capability reviews
- Familiarity with relevant frameworks and standards (MITRE ATT\&CK, NIST CSF, CIS Controls, etc.)
- Demonstrated success in building, leading, and scaling consulting services
- Strong executive presence and comfort engaging with CISOs, CTOs, and Boards
- Excellent written and verbal communication skills, with a record of influencing clients and internal stakeholders
- Knowledge of Palo Alto Networks products (e.g. Cortex suite) and how they support best in class security operations
- Ability to travel as needed to meet business demands (on average 25%)
- Bachelor’s Degree in Information Security, Computer Science, Digital Forensics, Cybersecurity, or equivalent professional experience required
Preferred Qualifications
- Master’s Degree or cybersecurity certifications (CISSP, CISM, GIAC, etc.) preferred
- Experience advising CISOs and other senior stakeholders on strategic planning, resource prioritization, capability development, and cybersecurity roadmaps
- Proven experience managing diverse teams of business and technical consultants
- Ability to scope new consulting opportunities, including drafting statements of work, proposals, and resource estimates
- Background in scripting, automation, or use of command\-line tools in assessments is a plus
- Public speaking, training, and enablement experience is strongly preferred
- 10\+ years of experience building, strengthening, and expanding long\-term client relationships
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.
$236,000\.00 \- $275,000\.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 accommodations@paloaltonetworks.com.
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 $236K-$275K range is above the 75th percentile 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. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($255K) sits 17% above the category median. Disclosed range: $236K to $275K.
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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