Interested in this AI/ML Engineer role at Zoom Communications?
Apply Now →Skills & Technologies
About This Role
- Seattle, Washington, United States
- Full time
Immigration sponsorship is not available for this positionWhat you can expect:
As a Machine Learning Engineer on our GenAI Engineering team, you will play a critical role in shaping the future of Zoom AI through innovative engineering solutions. You'll take the lead in developing and enhancing AI algorithms, techniques, and solutions for our AI Companion product. Working closely with experienced researchers and engineers, you'll be part of a dynamic team dedicated to achieving groundbreaking results in agentic AI.
Responsibilities:
- Architectural Leadership
+ Design and evolve the architecture of ZVA and AIEA runtime services to ensure scalability, reliability, and performance.
+ Define and implement the agent runtime framework that supports multi\-turn conversation understanding, contextual reasoning, and intelligent response generation.
+ Collaborate with cross\-functional teams to integrate LLM\-based reasoning and action orchestration into production systems.
- Team Management
+ Lead and mentor a distributed team of AI researchers, software engineers, and system architects across multiple regions.
+ Foster a culture of innovation, collaboration, and technical excellence.
+ Oversee project planning, resource allocation, and performance management to ensure timely delivery of high\-quality solutions.
- Technical Strategy and Innovation
+ Drive the technical roadmap for ZVA AI and AIEA runtime services in alignment with organizational goals.
+ Evaluate emerging AI technologies and frameworks to enhance the team’s capabilities in conversational AI and agent intelligence.
+ Ensure best practices in model deployment, runtime optimization, and system observability.
- Cross\-Functional Collaboration
+ Partner with product, research, and infrastructure teams to align AI capabilities with business objectives.
+ Communicate technical vision and progress to stakeholders and leadership teams.
+ Promote knowledge sharing and technical alignment across global engineering teams.
What we're looking for:
Required a bachelor's degree Computer Science, Machine Learning, Distributed Systems, a related field, or a foreign degree equivalent. Requires 5 years of experience in job offered or related occupation. Must have 5 years of experience in the following:
- 5 years of experience utilizing programming languages Python, C, C\+\+, or CUDA;
- 5 years of experience building scalable software systems;
- 5 years of experience utilizing deep learning frameworks such as PyTorch and TensorFlow;
- 5 years of experience developing and deploying business application logic for handling requests and routing to deployed machine learning models such as LLMs, as well as, feature stores in DynamoDB, MongoDB, MySQL, and ElasticSearch;
- 5 years of experience leveraging authentication mechanisms such as Asymmetric JWT using a complex and securely designed key management system, such as AWS KMS and internally developed CSMS;
- 5 years of experience leveraging machine learning models, such as LLM prompts and agentic workflows, internally developed and third\-party, and deploy them in a microservice environment powered by Docker and Kubernetes (AWS EKS), and an internal platform called ZCP, using languages such as Python and Java;
- 5 years of experience writing machine learning workflows operating on up to terabytes of data, deploy, and verify them on DataBricks using technologies like Spark and SQL.
- Telecommuting work arrangement permitted: position may work in various unanticipated locations throughout the U.S. Position does not require domestic or international travel.
Zoom Communications, Inc.
\#LI\-DNI
\#Ind0
Salary Range or On Target Earnings:
Minimum:
$214,080\.00
Maximum:
$332,200\.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.
Ways 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.
We welcome people of different backgrounds, experiences, abilities and perspectives including qualified applicants with arrest and conviction records and any qualified applicants requiring reasonable accommodations in accordance with the law.
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.
Think of this opportunity as a marathon, not a sprint! We're building a strong team at Zoom, and we're looking for talented individuals to join us for the long haul. No need to rush your application – take your time to ensure it's a good fit for your career goals. We continuously review applications, so submit yours whenever you're ready to take the next step.
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.
Salary Context
This $214K-$332K 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 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 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($273K) sits 25% above the category median. Disclosed range: $214K to $332K.
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.
Zoom Communications AI Hiring
Zoom Communications has 8 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer, AI Software Engineer. Positions span Seattle, WA, US, Remote, US, San Jose, CA, US. Compensation range: $271K - $387K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% above the national 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.