Description
Workday Government focuses on outcomes serving a larger mission, supporting U.S. federal agencies in modernizing and transforming employee lifecycle experiences and finance operations.
The Workday ML Runtime team seeks a Software Engineer to design, implement, and deliver highly scalable features for the Machine Learning Runtime platform. You will partner with Data Scientists, ML Engineers, and other Software Engineers to create technology bringing these features to life.
Key Responsibilities:
- Developing frameworks, automation, and tooling to foster efficiency and innovation.
- Applying technologies like Kubernetes, Docker, and Python to enhance developer scalability in creating ML Runtime Inference applications.
- Implementing and operating distributed systems and software development, including conception, specification, design, programming, documentation, testing, and bug fixing.
- Developing products and services empowering developers to streamline interactions with the ML platform.
- Working with public clouds (IAAS, AWS, GCP) and applying capacity management principles.
- Deploying and orchestrating containers in production environments using technologies like Containers, Kubernetes, Service Mesh, ArgoCD, and related tools.
- Collaborating with Tech Leads and ML Engineers to elaborate on requirements and drive technical solutions.
- Owning and developing features from end to end, including infrastructure as code.
- Researching, evaluating, prototyping, and driving adoption of new ML tools with reliability and scale in mind.
- Proactively addressing and resolving issues, automating processes, and empowering engineers to self-service their operational needs.
- Availability for on-call support on a rotational basis.
Requirements:
- 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.
Preferred Qualifications:
- Hands-on experience designing, deploying, and scaling Machine Learning runtime platforms and inference pipelines.
- Proficiency with Infrastructure as Code (e.g., Terraform), Git SCM workflows, and GitOps/CD engines (e.g., ArgoCD, Jenkins).
- Experience building end-to-end monitoring, metrics, and alerting pipelines using telemetry stacks like Grafana or Prometheus.
- Strong understanding of distributed systems, SaaS microservices, Object-Oriented Design (OOD), and automated testing methodologies.
- Proven experience leading technical initiatives and mentoring team members.
Pay Information:
- Primary Location Base Pay Range: $163,800 USD - $245,800 USD
- Additional US Location(s) Base Pay Range: $148,200 USD - $264,000 USD