Senior Software Engineer - Core AI
Qualtrics
Seattle, WA, US
Onsite
2026-07-04
Announced salary
$168,000 - $221,000
Low
$107K
Median
$149K
High
$192K
Market in Seattle · BLS OEWS 2025
Estimated net pay
$10,508 - $13,679
/month · 25% withheld
after tax & contributions · Single, no dependents
Job description
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.
**Senior Software Engineer \- Socrates Experimentation Platform (SEP)**
**Why We Have This Role**
* Qualtrics is seeking a Senior Software Engineer to join the Socrates Experimentation Platform (SEP) team, part of the Core AI Product Unit.
* Our mission is to enable applied scientists, data scientists, and ML engineers to rapidly build, deploy, and operate AI features and solutions at scale — by providing a unified platform and a governed data architecture.
* We build and maintain the infrastructure and workflows that power the end\-to\-end ML lifecycle: data ingestion and anonymization, feature engineering, model training and evaluation, and production deployment — all within AWS SageMaker Unified Studio.
* We reduce time\-to\-value, improve data and model quality, and lower total cost of ownership through pur