Interested in this AI/ML Engineer role at Zoom Communications?
Apply Now →About This Role
- R19432
- Remote, United States
- Cyber Security \& Risk
- Full time
What you can expect
You'll own risk assessment across four AI governance domains: GenAI Security, Agentic AI Security, Shadow AI detection, and AI Gateway/Model Context Protocol governance, identifying where emerging AI use cases create exposure. Working with AI/ML teams and security engineering, you'll define the controls, tooling, and standards that enable Zoom to adopt AI safely at scale. You'll operationalize the AI Governance Risk Charter while contributing your security engineering judgment across network, endpoint, cloud, and data security domains.
About the Team
Zoom's Enterprise Security team is building the security foundation for how AI is adopted, deployed, and governed across Zoom. Our team is lean and multi\-domain, operating across AI governance, cloud, network, endpoint, and data security. We solve emerging risks through collaboration, shared ownership, and the ability to flex across domains rather than staying in narrow specializations.
Responsibilities
- Conducting risk evaluations of AI use cases across GenAI, agentic AI, shadow AI, and AI gateway/MCP domains, scoring exposure using Likelihood and Impact methodology and maintaining the AI governance risk register.
- Identifying security gaps in GenAI deployments including prompt injection, data leakage, model misuse, and output handling, and recommending targeted controls.
- Assessing agentic AI systems for risks around autonomy, tool access, privilege escalation, and unintended action, and defining monitoring requirements.
- Detecting and evaluating shadow AI and local/unsanctioned AI usage, quantifying risk and recommending detection and enforcement approaches.
- Evaluating AI security tooling including AI gateways, CASB/SWG, Model Context Protocol governance solutions, and agentic monitoring platforms for functional requirements and integration.
- Contributing to security frameworks, architectural standards, and policies that translate the AI Governance Risk Charter into concrete technical requirements and control implementations.
- Applying security engineering judgment across network, endpoint, cloud, and data security domains to support architecture reviews, risk assessments, and incident response.
- Partnering with AI/ML, engineering, and enterprise teams through collaborative working groups to identify security solutions and tracking execution.
What we’re looking for
- Have 5\+ years of experience in security engineering, cybersecurity, or a closely related field, demonstrated understanding of cybersecurity frameworks, compliance requirements, and emerging threat landscapes.
- Bring hands\-on experience with risk assessment and risk management methodologies, including impact\-based prioritization.
- Possess working knowledge of modern AI/ML systems and the security risks they introduce (LLMs, generative AI, agentic systems). Have working knowledge of core security domains beyond AI (e.g., network, endpoint, cloud, or data security) sufficient to contribute to architecture reviews and risk assessments.
- Bring experience securing GenAI, agentic AI, or LLM\-based applications in enterprise environments would be a bonus. As well as familiarity with AI gateways, Model Context Protocol (MCP), CASB/SWG, or DLP tooling.
- Have experience detecting and governing shadow IT / shadow AI usage would be advantageous. As well as threat modeling experience applied to AI or novel technology environments.
- Leverage experience contributing to incident response, detection engineering, or purple team exercises would be a bonus.
Salary Range or On Target Earnings:
Minimum:
$98,900\.00
Maximum:
$228,700\.00
In addition to the base salary and/or OTE listed Zoom has a Total Direct Compensation philosophy that takes into consideration; base salary, bonus and equity value.
Note: Starting pay will be based on a number of factors and commensurate with qualifications \& experience.
We also have a location based compensation structure; there may be a different range for candidates in this and other locations
At Zoom, we offer a window of at least 5 days for you to apply because we believe in giving you every opportunity. Below is the potential closing date, just in case you want to mark it on your calendar. We look forward to receiving your application!
Anticipated Position Close Date:
08/06/26Ways of Working
Our structured hybrid approach is centered around our offices and remote work environments. The work style of each role, Hybrid, Remote, or In\-Person is indicated in the job description/posting.
Benefits
As part of our award\-winning workplace culture and commitment to delivering happiness, our benefits program offers a variety of perks, benefits, and options to help employees maintain their physical, mental, emotional, and financial health; support work\-life balance; and contribute to their community in meaningful ways.
About Us
Zoomies help people stay connected so they can get more done together. We set out to build the best collaboration platform for the enterprise, and today help people communicate better with products like Zoom Contact Center, Zoom Phone, Zoom Events, Zoom Apps, Zoom Rooms, and Zoom Webinars.
We’re problem\-solvers, working at a fast pace to design solutions with our customers and users in mind. Find room to grow with opportunities to stretch your skills and advance your career in a collaborative, growth\-focused environment.
Our Commitment
At Zoom, we believe great work happens when people feel supported and empowered. We’re committed to fair hiring practices that ensure every candidate is evaluated based on skills, experience, and potential. If you require an accommodation during the hiring process, let us know—we’re here to support you at every step.
If you need assistance navigating the interview process due to a medical disability, please submit an Accommodations Request Form and someone from our team will reach out soon. This form is solely for applicants who require an accommodation due to a qualifying medical disability. Non\-accommodation\-related requests, such as application follow\-ups or technical issues, will not be addressed.
Our interviews are supported by BrightHire, a tool that helps us create a consistent and thoughtful interview experience and may include recordings. Please refer to our candidate privacy statement for more information of how we use your data.
\#LI\-Remote
Salary Context
This $98K-$228K range is below the median 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 Zoom Communications, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($163K) sits 24% below the category median. Disclosed range: $98K to $228K.
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.
Zoom Communications AI Hiring
Zoom Communications has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Jose, CA, US, Seattle, WA, US, Remote, US. Compensation range: $228K - $451K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 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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