Workday
Mid / Senior Software Development Engineer - ML Ops (US Federal)
$148K–$264K
USA.VA.Reston · Flex · Senior · Full-time · Posted Sep 29, 2026
- Experience
- P4: 7+ years in each of the stated DevOps/automation/CI/CD and Python/container-orchestration areas. P3: 5+ years in each of the stated DevOps/automation/CI/CD and Python/containerization areas.
- Education
- Bachelor’s degree in Computer Science, STEM field, or equivalent practical experience.
- Visa sponsorship
- Personnel working on the U.S. federal government contracts must be United States citizens (naturalized or native).
About the job
Designs and operates scalable ML runtime infrastructure, inference services, and developer tooling for Workday’s machine-learning platform supporting U.S. federal customers.
What you'll do
- Build and maintain frameworks, automation, and developer tools for ML runtime services and inference applications.
- Develop and operate distributed systems, containerized production workloads, and infrastructure as code across public clouds.
- Partner with ML and engineering teams to deliver reliable, scalable platform features; participate in rotational on-call support.
What they ask for
- 7+ years of DevOps engineering, infrastructure automation, and CI/CD pipeline development for the P4 Sr. SDE level, or 5+ years for the P3 SDE level.
- 7+ years of Python and container orchestration experience for P4, or 5+ years of Python and containerization experience for P3.
- Bachelor’s degree in Computer Science or a STEM field, or equivalent practical experience.
- U.S. citizenship; ability to obtain and maintain a U.S. government security clearance.
Nice to have
- Active TS/SCI w/CI Poly clearance.
- Hands-on MLOps experience; Infrastructure as Code, GitOps/CD, observability, distributed systems, and automated testing experience.
Pay
$163,800 USD - $245,800 USD (base salary); $148,200 USD - $264,000 USD (base salary)
Tools
Kubernetes, Docker, Python, AWS, GCP, ArgoCD, Jenkins, Terraform
AI focus
Develop and operate ML runtime platforms, inference applications, and pipelines; evaluate and adopt ML tools with reliability and scale in mind.
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