Interested in this AI/ML Engineer role at Wiz Soluções?
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About This Role
Remote - USA | Sales
Come join the organization that is redefining security for the AI era. As one of the fastest-growing startups ever, we enable teams to secure cloud and AI applications by connecting code, cloud, and runtime into a single shared context. Trusted by security teams all over the world, we have a proven track record of success and a culture that values world-class talent. Not to mention, we're now powered by Google, meaning we offer our customers an AI-powered platform that harnesses Google’s Threat Intelligence and Security Operations to better detect, prevent, and respond to threats across all environments, allowing for further innovation.
Our Wizards from all over the globe work together to protect the infrastructure of our customers, including over 65% of the Fortune 100, who trust us to scan and secure over 230 billion files daily. We’re honored to be a leading player in a massive and growing market, and we continue to look for exceptional Wizards who are eager to make a significant impact on our team. At Wiz, you’ll have the freedom to think creatively, dream big, and use your full range of skills to contribute to our momentous growth. Come join our team and help us create secure cloud environments that allow even the best companies to move faster, all while having some fun!
Minimum qualifications
- 5 years of experience in technical enablement, GTM operations, RevOps, or AI content management in a B2B SaaS environment.
- Experience building, configuring, or managing enterprise AI chatbots, agentic workflows, or AI-driven knowledge platforms.
- Experience with prompt engineering and LLM behavior.
- Experience with workflow automation tools (e.g., Zapier, Make) and connecting disparate systems via APIs to build content feedback loops.
- Experience managing cross-functional programs, juggling multiple priorities simultaneously, and delivering against tight deadlines.
Preferred qualifications
- Experience in a cybersecurity environment.
- Deep understanding of GTM motions, sales enablement best practices, and content lifecycle management.
- Familiarity with modern sales enablement platforms and CMS architecture.
- Ability to use analytics to measure tool adoption, improve program outcomes, and prove ROI on AI initiatives.
- Strong ability to navigate ambiguity, experiment with bleeding-edge technology, and rapidly shift priorities.
Responsibilities
- Develop the strategy and infrastructure to power GTM content for our enterprise AI chatbot platform, transforming static content into dynamic, on-demand enablement.
- Design, build, and deploy customized AI "skills" that leverage our internal content repository to surface hyper-relevant, just-in-time enablement materials and deal-specific insights for Account Executives (AEs).
- Architect prompt frameworks and AI workflows that automatically generate customer-facing assets, meeting prep materials, and competitive talk tracks based on real-time deal context.
- Build and maintain agentic workflows that proactively ping content owners, automate feedback loops, and flag outdated materials to ensure all source data remains governed, accurate, and consistently fresh for AI ingestion.
- Develop a robust GTM content framework, optimizing taxonomy, tagging, and metadata specifically for AI searchability and Retrieval-Augmented Generation (RAG).
- Partner with cross-functional leaders to identify content gaps and align AI-driven deliverables to business priorities, including new product introductions, onboarding, and strategic campaigns.
- Build and manage the GTM content calendar to seamlessly align with sales cycles and product marketing campaigns, while leading end-to-end content programs.
- Optimize the discoverability and usability of content within our AI search experiences and broader enablement tech stack.
- Collaborate with the data and analytics team to measure the effectiveness of AI skills and generated assets (e.g., usage, deal impact, time-saved), and continuously refine prompt architecture based on field feedback.
- Provide regular enablement communications, highlighting new AI capabilities, automated workflows, and high-value content.
- Ensure all content meets brand, messaging, and compliance standards before being integrated into our AI ecosystem.
Compensation + Benefits
Compensation for this full-time position includes base salary + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
The US base salary range for this full-time position is listed below.
US Base Pay Range $136,000—$185,000 USD Applicants must have the legal right to work in the country where the position is based, without the need forvisa sponsorship.This role does not offervisasponsorship*.*
Wiz is an equal opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics.
By submitting your application, you acknowledge that Wiz will process your personal data in accordance with Wiz's Privacy Policy.
The application deadline is: 11/06/2026
Salary Context
This $136K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Wiz Soluções, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($160K) sits 24% below the category median. Disclosed range: $136K to $185K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Wiz Soluções AI Hiring
Wiz Soluções has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $185K - $185K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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