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Seattle, Washington, United States Category Engineering, Product, \& UX Design
Job description
At Qualtrics, we create software the world’s best brands use to deliver exceptional frontline experiences, build high\-performing teams, and design products people love. But we are more than a platform—we are the creators and stewards of the Experience Management category serving over 18K clients globally. Building a category takes grit, determination, and a disdain for convention—but most of all it requires close\-knit, high\-functioning teams with an unwavering dedication to serving our customers.
When you join one of our teams, you’ll be part of a nimble group that’s empowered to set aggressive goals and move fast to achieve them. Strategic risks are encouraged and complex problems are solved together, by passing the mic and iterating until the best solution comes to light. You won’t have to look to find growth opportunities—ready or not, they’ll find you. From retail to government to healthcare, we’re on a mission to bring humanity, connection, and empathy back to business. Join over 5,000 people across the globe who think that’s work worth doing.
AI Security Architect
Why We Have This Role
AI systems face threats that traditional security architecture wasn't built for: adversarial manipulation, model theft, data poisoning, prompt injection, and supply\-chain risk across the ML pipeline. The AI Security Architect leads the design and implementation of security frameworks that protect our AI systems — building resilience against these threats and earning our customers' trust.
How You’ll Find Success
- Strategic Visionary: You hold a holistic view of AI/ML threat models, proactively identifying and mitigating emerging risks. You guide security architecture strategy so it aligns with the organization’s risk posture and business goals.
- Technical Innovator: Your drive for continuous improvement pushes you to explore and implement cutting\-edge AI security practices — adversarial robustness testing, model hardening, secure MLOps — keeping our AI systems resilient against evolving threats.
- Collaborative Leader: You build partnerships across security, engineering, and data science, promoting best practices in AI security. Your ability to foster collaboration creates an informed, cohesive environment dedicated to protecting our systems.
- Resilient and Adaptive: You thrive in a fast\-paced environment, managing complex projects while guiding cross\-functional teams through shifts in the threat landscape or technology. Your problem\- solving skills are essential to navigating the complexities of AI security governance.
How You’ll Grow
- Shape Industry Standards: Participate in industry conferences, thought leadership forums, and professional organizations to influence the future of AI security practices.
- Executive Presence: Increase your visibility and involvement in executive\-level discussions, refining how you communicate strategic security insights.
- Expand Your Leadership Toolkit: As a thought leader in AI security architecture, mentor and coach emerging talent, growing the security expertise across the organization.
- Complex Problem\-Solving: Tackle significant AI security challenges that sharpen your analytical,critical thinking, and strategic planning abilities.
Things You’ll Do
- Drive Innovation in AI Security: Lead research, evaluation, and implementation of next\-generation security technologies and methodologies — adversarial defense, model security, secure MLOps pipelines — that support our AI solutions.
- Conduct Security Reviews: Perform security reviews of AI products and proposed designs, including architectures built on Model Context Protocol (MCP) and agentic AI systems, identifying risks such as tool\-use abuse, unauthorized action\-taking, and insecure agent\-to\-agent or agent to\-tool communication.
- Develop Standards \& Reference Architectures: Author AI\-related technical security standards, guardrails, and reference architectures that give product and engineering teams a secure, repeatable blueprint for building and deploying AI systems.
- Shape Security Strategy: Develop a comprehensive AI security strategy that aligns with the organization’s risk profile and business objectives.
- Manage Complex Projects: Oversee execution of large\-scale AI security initiatives, ensuring on\-time, on\-budget delivery while proactively addressing risks and fostering adaptability within teams.
- Foster a Culture of Excellence: Create an environment that promotes knowledge sharing, collaboration, and continuous learning. Mentor colleagues to build a high\-performing team committed to our security objectives.
What We’re Looking For On Your Resume
- While we value the wealth of experience, we put more emphasis on your capability and the outcome you’ve produced. For this role, these elements are particularly important:
- Extensive Architectural Expertise: 5\+ years of experience in AI/ML security, cybersecurity architecture, or a related field, with a proven history of designing secure architectures for AI systems; capable of leading strategic initiatives to strengthen security posture.
- Technical Expertise: Strong understanding of AI/ML security adversarial machine learning, model security, threat modeling, secure MLOps — and familiarity with encryption and secure system design.
- Demonstrated Initiative Leadership: Proven experience leading large scale AI security initiatives, coordinating cross\-functional teams to align with security guidelines while driving operational efficiency and business growth.
- Strategic Mindset: Ability to translate AI security challenges into actionable solutions that integrate with business objectives and risk management frameworks.
- Collaboration \& Communication Skills: Excellent communication abilities to engage effectively with both technical and non\-technical stakeholders, fostering constructive partnerships across the organization.
What You Should Know About This Team
- Joining Qualtrics means becoming part of a team bold enough to chase breakthrough experiences \- like building a technology that will be a force for good. A team committed to diversity, equity, and inclusion because of a conviction that every voice holds value, with a vision for representation that matches the world around us and inclusion that far exceeds it.
- You could belong to a team whose values center on transparency, being all in, having customer obsession, acting as one team, and operating with scrappiness. All so you can do the best work of your career.
- The direct impact on customer success through our work is exceptionally fulfilling, offering valuable opportunities for rapid learning and career advancement.
Our Team’s Favorite Perks and Benefits
- Wellness Reimbursement for $300 per quarter for wellness activities including gym memberships, spa massages, workout equipment, meditation apps, and much more.
- $1800 Experience bonus to be used for an “Experience” of your choosing
- Amazing QGroup Communities; MOSAIQ, Green Team, Qualtrics Pride, Q\&Able, Qualtrics Salute, and Women’s Leadership Development, which exist as places for support, allyship, and advocacy.
The Qualtrics Hybrid Work Model: Our hybrid work model is elegantly simple: we all gather in the office three days a week; Mondays and Thursdays, plus one day selected by your organizational leader. These purposeful in\-person days in thoughtfully designed offices help us do our best work and harness the power of collaboration and innovation. For the rest of the week, work where you want, owning the integration of work and life. \#hybrid
*Qualtrics is an equal opportunity employer meaning that all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other protected characteristic.*
*Applicants in the United States of America have rights under Federal Employment Laws:Family \& Medical Leave Act,* *Equal Opportunity Employment**,* *Employee Polygraph Protection Act*
*Qualtrics is committed to the inclusion of all qualified individuals. As part of this commitment, Qualtrics will ensure that persons with disabilities are provided with reasonable accommodations. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please let your Qualtrics contact/recruiter know.*
*Not finding a role that’s the right fit for now? Qualtrics Insiders is the one\-stop shop for all things Qualtrics Life. Sign up for exclusive access to content created with you in mind and get the scoop on what we have going on at Qualtrics \- upcoming events, behind the scenes stories from the team, interview tips, hot jobs, and more. No spam \- we promise! You'll hear from us two times a month max with fresh, totally tailored info \- so be sure to stay connected as you explore your best role and company fit.*
*For full\-time positions*, this pay range is for base per year; however, base pay offered within this range may vary depending on location, job\-related knowledge, education, skills, and experience. A sign\-on bonus and restricted stock units may be included in an employment offer. Full\-time employees are eligible for medical, dental, vision, life and disability, 401(k) with match, paid time off, a wellness reimbursement, mental health benefits, and an experience bonus.
Washington State Base Annual Pay Transparency Range
$168,000—$221,000 USD
Salary Context
This $168K-$221K range is above 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 Qualtrics, 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 ($194K) sits 9% below the category median. Disclosed range: $168K to $221K.
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.
Qualtrics AI Hiring
Qualtrics has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $221K - $221K.
Location Context
AI roles in Seattle pay a median of $228,700 across 516 tracked positions. That's 6% 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 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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