Senior Software Engineer - Core AI

Qualtrics Qualtrics · Seattle · Seattle, WA · Engineering

Senior Software Engineer to join the Socrates Experimentation Platform (SEP) team, part of the Core AI Product Unit. The 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. The team builds and maintains 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. They aim to reduce time-to-value, improve data and model quality, and lower total cost of ownership through purpose-built self-service tooling, pipeline automation, and robust operational guardrails.

What you'd actually do

  1. Design and build scalable, reliable backend services and data pipelines that power the end-to-end ML lifecycle within AWS SageMaker Unified Studio.
  2. Own the entire lifecycle of the features and platform capabilities you work on, from ideation to delivery, collaborating closely with applied scientists, ML engineers, and product managers.
  3. Build and operate data infrastructure implementing a medallion (bronze/silver/gold) architecture — including ingestion, transformation, anonymization, aggregation, quality checks, and lineage.
  4. Operate production platform services with 99.9% availability SLOs, participating in on-call rotations and operational review processes.
  5. Drive self-service tooling and automation that reduces manual toil and accelerates onboarding of new AI projects.

Skills

Required

  • 4+ years of relevant software engineering experience.
  • Strong backend development experience in Python
  • Experience building and operating data pipelines or distributed data systems (e.g. ETL/ELT, event-driven architectures, SQS/Kafka, S3-based data lakes).
  • Experience with professional software engineering practices such as unit testing, code reviews, and writing design documents.
  • Experience owning deployment and operations for large-scale distributed systems.
  • Bachelor's degree in Computer Science or related field, or suitable industry experience.

Nice to have

  • familiarity with other languages such as Java, TypeScript, or Go is a plus.
  • Familiarity with AWS cloud services; hands-on experience with SageMaker, DataZone, Lake Formation, Glue, or similar data/ML platform services is a strong plus.
  • Familiarity with infrastructure-as-code (e.g. AWS CDK).
  • Familiarity with different database technologies across SQL and NoSQL options.

What the JD emphasized

  • rapidly build, deploy, and operate AI features and solutions at scale
  • unified platform
  • governed data architecture
  • end-to-end ML lifecycle
  • data ingestion and anonymization
  • feature engineering
  • model training and evaluation
  • production deployment
  • AWS SageMaker Unified Studio
  • purpose-built self-service tooling
  • pipeline automation
  • robust operational guardrails
  • 99.9% availability SLOs

Other signals

  • ML lifecycle platform
  • data ingestion and anonymization
  • feature engineering
  • model training and evaluation
  • production deployment
  • AWS SageMaker Unified Studio
  • governed data architecture
  • self-service tooling
  • pipeline automation
  • operational guardrails