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About This Role
Remote - USA | Support
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:
- 2+ years of experience leading or managing a team of internal tool developers, automation engineers, or systems developers, driving technical direction, scheduling and running Agile sprints, and managing epics and defects in Jira while staying actively hands-on in the code.
- 5+ years of hands-on development experience building internal tools, backend services, APIs, and web applications using Python and TypeScript/JavaScript (e.g., Node.js, React).
- Hands-on experience with Zendesk and Salesforce administration and development, including owning Zendesk configurations and apps, writing Salesforce Apex code, working with custom objects and workflows, and integrating with platforms like ServiceNow.
- Practical experience deploying and maintaining cloud-hosted services on GCP (or AWS/Azure) using containers, relational databases (e.g., PostgreSQL), data warehouses (e.g., BigQuery), and message queues.
- Hands-on experience using AI models, APIs, prompt engineering, and AI-assisted coding tools to build internal applications, automate operational workflows, and speed up the development lifecycle.
- Experience with everyday development and deployment tooling, including Terraform, Docker, Git, CI/CD pipelines, and managing changes across both cloud services and CRM sandboxes.
- Candidates must meet EAR part 772 and ITAR 120.15 definition of a U.S. person (Any individual who is granted U.S. citizenship; or any individual who is granted U.S. permanent residence (green card holder); or any individual who is granted status as a "protected person") and reside in the contiguous United States.
Preferred qualifications:
- Experience connecting AI tools and workflows to internal systems using Model Context Protocol (MCP) servers, tool proxies, and AI models and coding tools (such as Gemini and Claude Code).
- Experience building custom UI components, embedded apps, and integrations across Zendesk, Salesforce, and ServiceNow, such as Zendesk Apps Framework (ZAF) apps and Salesforce Lightning Web Components (LWC).
- Track record of building practical productivity tools for internal teams, such as Chrome extensions, VS Code extensions, or cloud-hosted workspaces.
- Experience building ticket routing, scheduling, and team availability tools that factor in queues, engineer skills, and global shift coverage.
- Familiarity with integrating workplace and support tools like Slack (bots and workflows), Jira, Zoom, and BI reporting pipelines.
- Prior customer-facing experience in a technical support role, preferably at a B2B technology company.
About the job:
At Wiz, technical support is never an afterthought. It is core to how our platform functions and a key part of how we create magic for our customers every day. To deliver a truly magical support experience at global scale, we put AI at the forefront of how we operate. As a Manager of Applied AI, Solutions Support Engineering, you will be an AI-first technical leader guiding the team of internal tool and automation developers who build the tools and architecture that make this experience possible.
Rather than managing customer-facing support engineers directly, you will lead the builders behind our support platform ecosystem. You will own our primary Zendesk ticketing environment, build integrations with Salesforce and ServiceNow, and lead the development of our intelligent ticket routing, browser and IDE extensions, and automated triage, pre-investigation, and resolution workflows.
You will thrive in this role not only by leading and mentoring your team, but by getting your hands directly into the technology. Leading from the front, you will split your time between writing and reviewing code alongside your team, using AI models and tools like Gemini and Claude Code to build internal tools and run our development pipeline, owning our Zendesk platform, and coaching the developers who power our support organization.
Responsibilities:
- Lead, mentor, and guide an AI-first team of internal tool and automation developers, scheduling and running sprints in Jira, managing epics and defects, and driving day-to-day technical execution and team growth.
- Own the tooling and automation roadmap that powers our technical support experience, designing the tools and architecture that handle the heavy lifting of case resolution.
- Serve as a hands-on technical leader, actively writing, reviewing, and shipping code in Python, TypeScript/JavaScript, and Apex across internal web apps, background automation jobs, and CRM integrations.
- Champion an AI-first development pipeline by using AI models, APIs, and coding tools (like Gemini and Claude Code) to build internal tools faster, power automated ticket triage and pre-investigation workflows, and maintain high quality and reliability across our automations.
- Own the administration, architecture, and custom development of our Zendesk platform (including embedded apps, webhooks, declarative workflows, and routing/SLA logic), while building custom integrations and workflows across ServiceNow and Salesforce (including Apex code).
- Lead the development and maintenance of our internal support tooling suite, including skills- and availability-based ticket routing, browser and IDE extensions, and integrations with Slack, Jira, Zoom, and PagerDuty.
- Maintain the cloud infrastructure that runs our support tools and automations on Google Cloud Platform (GCP) using containerized services, PostgreSQL, BigQuery, Pub/Sub, and Terraform while keeping access and customer data secure.
- Partner closely with Support leadership, frontline Solutions Support Engineers, Product, and the CTO team to spot workflow bottlenecks and build new tools and automations that keep AI at the forefront of our support experience.
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: 11/13/2026
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.
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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