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About This Role
Zscaler (NASDAQ: ZS) accelerates digital transformation so customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Distributed across 200+ public data centers globally and thousands of private sites at the edge, the SASE-based Zero Trust Exchange is the world’s largest in-line cloud security platform.
We believe the future of work is Human + AI and are building an AI-native enterprise where human potential is amplified by machine intelligence to solve the world’s hardest security challenges. Driven by deep customer obsession, we are committed to the mission, outcome, and to each other. We bring these commitments to life through three core behaviors: ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback. Ready to make an impact at the company pioneering security transformation in the AI era? Join us at Zscaler.
Principal AI Security Specialist
About The Role Zscaler is seeking an experienced AI Security Specialist to help customers securely adopt and operationalize AI across the enterprise. This role is designed for a customer-facing technical expert who combines the consultative strengths of a Solutions Engineer / Solutions Consultant with deep expertise in AI, GenAI architectures, and AI security.
The AI Security Specialist will partner closely with Sales, Solutions Engineering, Product, and customers to configure, demonstrate, and position Zscaler’s AI security solutions. This individual will serve as a trusted advisor on secure AI adoption, helping customers understand how to protect data, enforce policy, reduce risk, and enable safe use of AI technologies across users, applications, and workflows.
This is a highly strategic, technical, and field-facing role for someone who can engage in executive conversations, lead architecture discussions, deliver compelling demos, and support technical validation for complex enterprise opportunities.
What You’ll Do
- Partner with account teams and core Solutions Engineering teams to drive the technical strategy for AI Security-related opportunities.
- Engage customers in discovery around AI initiatives, GenAI use cases, enterprise architecture, security requirements, and adoption challenges.
- Configure and deliver compelling demos of Zscaler’s AI security solutions, tailored to customer priorities and business outcomes.
- Support proofs of concept, proofs of value, and technical evaluations for strategic AI-related opportunities.
- Advise customers on how to securely enable AI usage across the enterprise, including data protection, policy enforcement, user access, governance, and risk mitigation.
- Translate complex AI and security concepts into clear, actionable value for technical, security, and executive stakeholders.
- Serve as a subject matter expert on modern AI technologies, including large language models (LLM), retrieval-augmented generation (RAG), agents, prompt workflows, model usage patterns, and emerging enterprise AI architectures.
- Help customers understand the security implications of enterprise AI adoption, including sensitive data exposure, unsanctioned AI usage, governance gaps, and operational risk.
- Collaborate with Product and Engineering to provide field feedback, influence roadmap priorities, and improve product positioning based on customer needs.
- Develop reusable demos, technical assets, competitive positioning, and enablement content to help scale Zscaler’s AI field motion.
- Enable internal teams on AI trends, AI security use cases, market messaging, and customer objections.
- Represent Zscaler in executive briefings, customer workshops, webinars, and industry events related to AI security and secure AI transformation.
What We’re Looking For
- 5+ years of experience in a customer-facing technical role such as Sales Engineering, Solutions Consulting, Solutions Architecture, Customer Engineering, or Technical Advisory.
- Deep understanding of modern AI and GenAI technologies, including:
+ Large language models (LLMs) + Retrieval-augmented generation (RAG) + Agents, MCP and orchestration patterns + Prompt workflows + Model usage considerations, safety, and governance concepts
- Experience configuring, demonstrating, and presenting technical solutions in complex enterprise sales cycles.
- Strong ability to connect technical capabilities to business value, security outcomes, and customer priorities.
- Strong presentation, demo, and communication skills, with the ability to engage architects, practitioners, security leaders, and executives.
- Familiarity with enterprise architecture, cloud environments, APIs, application workflows, and integration patterns.
- Strong understanding of cybersecurity principles, with experience in one or more areas such as:
+ cloud security + data security + zero trust architecture + secure access + governance and policy controls
- Ability to work cross-functionally with Sales, Solutions Engineering, Product, Engineering, Marketing, and other go-to-market teams.
- Comfort operating in a fast-moving market where AI technologies, buyer expectations, and competitive positioning continue to evolve.
Preferred Qualifications
- Experience in AI security, secure AI adoption, model governance, or AI-related risk management.
- Experience helping enterprises evaluate or deploy AI technologies in production environments.
- Hands-on experience building prototypes, demo environments, or lightweight AI applications.
- Hands on experience with deploying infrastructure in public cloud
- Familiarity with enterprise concerns around AI usage, including data leakage, policy violations, compliance, third-party AI tools, and governance.
- Experience supporting strategic accounts and complex technical buying cycles.
- Background in Python, APIs, scripting, or solution prototyping.
- Experience creating reusable field assets such as demo content, reference architectures, technical workshops, and playbooks.
- Familiarity with security platforms, cloud-native architecture, and modern SaaS security models.
Success in This Role Looks Like
- You become a trusted advisor to customers on both AI innovation and AI risk reduction.
- You help customers understand how Zscaler enables secure, scalable adoption of AI across the enterprise.
- You deliver high-impact demos and technical engagements that connect Zscaler’s capabilities to real customer priorities.
- You improve technical win rates for AI-related opportunities through strong discovery, positioning, and technical validation.
- You help the field scale through reusable assets, crisp messaging, and practical AI security guidance.
- You provide actionable customer insight that sharpens product strategy and go-to-market execution.
Why This Role Matters
Enterprises are rapidly adopting AI, but they are doing so in an environment filled with security, governance, and data protection concerns. Zscaler is uniquely positioned to help customers enable AI safely through zero trust principles, secure access, policy enforcement, and protection of sensitive data.
The AI Security Specialist plays a critical role in that mission by helping customers move from AI experimentation to secure, enterprise-wide adoption with confidence.
At Zscaler, we are committed to building a team that reflects the communities we serve and the customers we work with. We foster an inclusive environment that values all backgrounds and perspectives, emphasizing collaboration and belonging. Join us in our mission to make doing business seamless and secure.
Benefits Our Benefits program is one of the most important ways we support our employees. Zscaler proudly offers comprehensive and inclusive benefits to meet the diverse needs of our employees and their families throughout their life stages, including:
- Various health plans
- Time off plans for vacation and sick time
- Parental leave options
- Retirement options
- Education reimbursement
- In-office perks, and more!
Learn more about Zscaler's hybrid working model and benefits here.
By applying for this role, you adhere to applicable laws, regulations, and Zscaler policies, including those related to security and privacy standards and guidelines.
Zscaler is committed to providing equal employment opportunities to all individuals. We strive to create a workplace where employees are treated with respect and have the chance to succeed. All qualified applicants will be considered for employment without regard to race, color, religion, sex (including pregnancy or related medical conditions), age, national origin, sexual orientation, gender identity or expression, genetic information, disability status, protected veteran status, or any other characteristic protected by federal, state, or local laws. See more information by clicking on the Know Your Rights: Workplace Discrimination is Illegal link. Pay Transparency
Zscaler complies with all applicable federal, state, and local pay transparency rules.
Zscaler is committed to providing reasonable support (called accommodations or adjustments) in our recruiting processes for candidates who are differently abled, have long term conditions, mental health conditions or sincerely held religious beliefs, or who are neurodivergent or require pregnancy-related support.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Zscaler, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Zscaler AI Hiring
Zscaler has 7 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Santa Clara, CA, US, Santa Clara, CA, Pennsylvania, United States. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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