Workday is looking for a Software Development Engineer - ML Ops to join our Payroll ML team. In this role, you will design, implement, and deliver highly scalable features that power Workday's Payroll Machine Learning products in production. You will partner closely with Data Scientists, ML Engineers, and other Software Engineers to create the technology that brings these features to life.

What You'll Do

Develop 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.
Implement and operate distributed systems and software development including conception, design, programming, testing, and bug fixing.
Develop products and services that empower developers to streamline their interactions with the ML platform.
Work with public clouds (such as IAAS, AWS, GCP) and apply capacity management principles.
Deploy and orchestrate 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.
Proactively address and resolve issues, automate processes, and empower engineers to self-service their operational needs.
Provide on-call support on a rotational basis.

What We're Looking For

US Citizenship is required.
3 or more years of DevOps experience including Infrastructure automation and building CI/CD pipelines.
Proficient in Python programming.
Strong skills in System design and writing comprehensive technical design docs.
Experience designing, implementing, and maintaining robust DevOps pipelines for deploying, monitoring, and scaling machine learning runtime environments.
Experience using technologies like Kubernetes/Docker to help developers scale their efforts in creating new products.
Ability to collaborate with other Machine Learning teams to improve both the product and engineering process efficiencies.

Nice to Have

Machine learning background.
Experience with communication protocols, RESTful services, service-oriented architecture, distributed systems, and microservices.
Experience building comprehensive monitoring services.
Prior experience with enterprise SaaS products.
Experience with monitoring tools like Grafana.
Passion for creating and maintaining documentation and fixing run books.
Availability for on-call support on a rotating basis.
Proficiency in infrastructure automation tools like Terraform, implementing CI/CD pipelines using Git and Jenkins, and applying continuous deployment tools such as ArgoCD.
BS/MS in Computer Science or a related technical field.
Excellent problem-solving skills with a focus on creating and maintaining accurate documentation.
Experience in leading or mentoring other team members and proven team collaboration experience.