Software Development Engineer - ML Ops with Security Clearance

About the Team
Your work matters here. At Workday Government, we focus on outcomes that serve a larger mission. Our work supports U.S. federal agencies as they modernize and transform the full employee lifecycle experience and finance operations—so they can operate with greater clarity, accountability, and trust. As a Fortune 500 company and a proven enterprise cloud platform, Workday brings modern technology, responsible AI, and secure infrastructure to some of the most complex environments in the world. The work isn’t theoretical. It’s operational. It’s high-impact.

And it demands rigor, integrity, and long-term thinking. From day one, you’ll be part of a team that values collaboration, follow-through, and doing the right thing—especially when the stakes are high. Our culture is grounded in integrity, respect, and shared responsibility. We challenge each other to think clearly, act thoughtfully, and build solutions that stand up to real-world demands. Here, curiosity is matched with accountability. Ambition is paired with trust.

You’ll have the space to do your best work, the support to keep growing, and the backing of a company committed to long-term investment in both its people and the federal mission. If you’re looking to apply your experience to meaningful, mission-driven work—alongside colleagues who take pride in building things that last—you’ll find that opportunity at Workday Government.

About the Role
Employees may be required to be on site at client locations in the DC, MD, and VA (DMV) area The Workday ML Runtime team is seeking an energetic and determined Software Engineer to design, implement, and deliver highly scalable features for our Machine Learning Runtime platform. As a member of this fast paced group you will have a unique and rewarding opportunity to shape and contribute towards microservices that power Workday Machine Learning features in production. You will partner with Data Scientists, ML Engineers, and other Software Engineers to create the technology that brings these features to life.

Key Responsibilities

• Developing frameworks, automation, and tooling to foster a culture of efficiency and innovation.
• Apply technologies like Kubernetes, Docker, and Python to enhance developer scalability in creating innovative ML Runtime Inference applications.
• Implementation and operation of distributed systems and software development including the conception, specifying, designing, programming, documenting, testing, and bug fixing involved in creating and maintaining applications, frameworks, or other software components.
• Developing products and services that empower developers to streamline their interactions with the ML platform.
• Working with public clouds (such as IAAS, AWS, GCP) and applying capacity management principles.
• Deploying and orchestrating containers in production environments, including technologies like Containers, Kubernetes, Service Mesh, ArgoCD and related tools.
• Actively engage with Tech Leads and ML Engineers across teams to elaborate on requirements and drive technical solutions.
• Own and develop features from end to end including infrastructure as code.
• Research, evaluate, prototype and drive adoption of new ML tools with reliability and scale in mind
• Strong dedication to proactively addressing and resolving issues, automating processes, and empowering engineers to self-service their operational needs for improved productivity.
• Availability for on-call support on a rotational basis. This role will support one or more direct or indirect contracts with the U.S. Federal Government which, due to federal government security requirements, mandates that all Workday personnel working on the contracts be United States citizens (naturalized or native). About You

This role may require a security clearance at the TS/SCI w/CI Poly level. Applicants must have the ability to obtain and maintain a U.S. government issued security clearance. An active TS/SCI w/CI Poly is preferred.

Basic Qualifications (P4 Sr. SDE)

• 7+ years of professional experience in DevOps engineering, infrastructure automation, and CI/CD pipeline development.
• 7+ years of experience with Python programming and container orchestration platforms (e.g., Docker, Kubernetes).
• Bachelor’s degree in Computer Science, STEM field, or equivalent practical experience. Other /

Preferred Qualifications

• MLOps & Domain Experience: Hands-on experience designing, deploying, and scaling Machine Learning runtime platforms and inference pipelines in partnership with ML teams.
• Tooling & Infrastructure: Proficiency with Infrastructure as Code (e.g., Terraform), Git SCM workflows, and GitOps/CD engines (e.g., ArgoCD, Jenkins).
• Observability: Experience building end-to-end monitoring, metrics, and alerting pipelines using telemetry stacks like Grafana or Prometheus.
• Architecture & Code Quality: Strong understanding of distributed systems, SaaS microservices, Object-Oriented Design (OOD), and automated testing methodologies (unit, integration, e2e).
• Leadership & Operations: Proven experience leading technical initiatives and mentoring team members; willingness to participate in a rotating on-call schedule. Basic Qualification (P3 SDE)
• 5+ years of professional DevOps experience, including infrastructure automation and CI/CD pipeline development.
• 5+ years of experience with Python programming and containerization technologies (e.g., Docker, Kubernetes).
• Bachelor’s degree in Computer Science, STEM field, or equivalent practical experience.

Other Qualifications

• MLOps Experience: Hands-on experience deploying, monitoring, and scaling Machine Learning runtime environments or pipelines alongside ML teams.
• Tooling & Infrastructure: Experience with Infrastructure as Code (e.g., Terraform), Git workflows, and GitOps/CD engines (e.g., ArgoCD, Jenkins).
• Observability: Experience building monitoring, metrics, and alerting systems using telemetry stacks such as Grafana or Prometheus.
• Software Fundamentals: Solid understanding of Object-Oriented Design (OOD), distributed systems, and SaaS microservice architectures.
• Quality & Operations: Familiarity with automated testing frameworks (unit, integration, e2e) and willingness to participate in a rotating on-call schedule.