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Workday

Builds software to manage HR and finances

Mid / Senior Software Engineer - ML Ops, US Federal

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeReston, VA, USAHybrid

About the Job

Requirements

  • At the P4 Senior Software Development Engineer level, candidates need 7 or more years of professional experience in DevOps engineering, infrastructure automation, and continuous integration and continuous delivery (CI/CD) pipeline development.
  • At the P4 Senior Software Development Engineer level, candidates need 7 or more years of experience programming in Python and using container orchestration platforms such as Docker and Kubernetes.
  • At the P4 Senior Software Development Engineer level, candidates need a Bachelor's degree in Computer Science, a STEM field, or equivalent practical experience.
  • At the P3 Software Development Engineer level, candidates need 5 or more years of professional DevOps experience, including infrastructure automation and CI/CD pipeline development.
  • At the P3 Software Development Engineer level, candidates need 5 or more years of experience programming in Python and using containerization technologies such as Docker and Kubernetes.
  • At the P3 Software Development Engineer level, candidates need a Bachelor's degree in Computer Science, a STEM field, or equivalent practical experience.
  • The role may require a security clearance at the Top Secret/Sensitive Compartmented Information with Counterintelligence Polygraph level. Applicants must be able to obtain and maintain a U.S. government-issued security clearance; an active clearance at that level is preferred.
  • Candidates must be United States citizens, naturalized or native, because the role supports U.S. federal government contracts.

Responsibilities

  • Develop frameworks, automation, and tooling to promote efficiency and innovation.
  • Apply technologies such as Kubernetes, Docker, and Python to improve developer scalability in creating machine learning runtime inference applications.
  • Implement and operate distributed systems and software, including conceiving, specifying, designing, programming, documenting, testing, and fixing bugs in applications, frameworks, and other software components.
  • Develop products and services that help developers streamline their interactions with the machine learning platform.
  • Work with public clouds such as infrastructure as a service, Amazon Web Services, and Google Cloud Platform, and apply capacity-management principles.
  • Deploy and orchestrate containers in production environments using containers, Kubernetes, service mesh, ArgoCD, and related tools.
  • Engage with technical leads and machine learning engineers across teams to elaborate on requirements and drive technical solutions.
  • Own and develop features end to end, including infrastructure as code.
  • Research, evaluate, and prototype new machine learning tools, and drive their adoption with reliability and scale in mind.
  • Proactively address and resolve issues, automate processes, and empower engineers to self-serve operational needs to improve productivity.
  • Provide on-call support on a rotating basis.

Desired Qualifications

  • Hands-on experience designing, deploying, and scaling machine learning runtime platforms and inference pipelines in partnership with machine learning teams.
  • Proficiency with infrastructure as code such as Terraform, Git source control management workflows, and GitOps/continuous delivery engines such as ArgoCD and Jenkins.
  • Experience building end-to-end monitoring, metrics, and alerting pipelines using telemetry stacks such as Grafana or Prometheus.
  • Strong understanding of distributed systems, software-as-a-service microservices, object-oriented design, and automated testing methodologies including unit, integration, and end-to-end testing.
  • Proven experience leading technical initiatives and mentoring team members.
  • Willingness to participate in a rotating on-call schedule.
  • Hands-on experience deploying, monitoring, and scaling machine learning runtime environments or pipelines alongside machine learning teams.
  • Experience with infrastructure as code such as Terraform, Git workflows, and GitOps/continuous delivery engines such as ArgoCD and Jenkins.
  • Experience building monitoring, metrics, and alerting systems using telemetry stacks such as Grafana or Prometheus.
  • Solid understanding of object-oriented design, distributed systems, and software-as-a-service microservice architectures.
  • Familiarity with automated testing frameworks for unit, integration, and end-to-end testing.
  • Willingness to participate in a rotating on-call schedule.

About the Company

Workday

Builds software to manage HR and finances

Workday builds cloud software that organizations use to manage employees, finances, and business plans. Its Human Capital Management and Financial Management applications handle processes such as hiring, payroll, expenses, accounting, and reporting, while Adaptive Planning helps teams build budgets and forecasts. Users work in connected applications with shared data and permissions, and Workday also sells AI tools that search information and carry out HR and finance tasks. Customers pay for subscriptions, with additional revenue from implementation, training, and other professional services. Workday was founded in 2005 and is headquartered in Pleasanton, California.

Company size
10,001+
Company stage
IPO
Headquarters
Pleasanton, CA
Founded
2005

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