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About the role
- Senior Staff Machine Learning Engineer at Workiva defining enterprise-level AI architecture and solutions. Leading technical direction and influencing secure AI platform design across multiple teams.
Responsibilities
- Own the architecture of Workiva’s AI platform and core AI services
- Shape how machine learning, Generative AI, and agentic systems are integrated across products
- Lead the move from early adoption to production-grade, enterprise-ready systems
- Define standards for model serving, retrieval, evaluation, governance, and platform reliability
- Lead the design of enterprise agentic systems, including orchestration, workflow execution, memory, and multi-agent coordination
- Design and evolve Retrieval-Augmented Generation capabilities for enterprise content and knowledge workflows
- Establish evaluation methods and quality frameworks for Generative AI applications
- Assess emerging AI technologies and guide adoption strategy for Workiva’s platform
- Influence technical direction across teams, products, and platform domains
- Mentor Staff and Senior Engineers and help raise the technical bar across the organization
- Partner closely with Product, Security, Infrastructure, and Architecture leaders
- Align teams around a shared vision for scalable, secure AI at Workiva
- Lead secure AI platform design, including authorization, runtime isolation, governance, auditability, and compliance
- Establish best practices for AI safety, model governance, and customer data protection
- Ensure AI systems meet enterprise expectations for availability, resiliency, observability, and operational support
- Design for fault tolerance and operational excellence in regulated, security-conscious environments
Requirements
- Bachelor’s degree in Computer Science, Engineering, or equivalent experience
- 10+ years of software engineering experience, including large-scale SaaS platforms
- 5+ years designing, deploying, and operating production ML, AI, or data-intensive systems
- Experience designing and operating enterprise AI platforms, including model serving, evaluation, observability, and governance
- Deep expertise in RAG, agentic systems, and large-scale knowledge systems
- Strong understanding of foundation model ecosystems, including inference, routing, prompting, and provider tradeoffs
- Experience with AI evaluation, secure AI systems, and regulated enterprise environments
- Proven track record leading architecture across multiple teams or platform domains
- Strong distributed systems, cloud-native, API, reliability, and operational excellence experience
- Expert-level Python proficiency and proficiency in at least one production language such as Java, Go, Scala, or C++
- Proven record mentoring senior engineers and technical leaders
Benefits
- Flexible working arrangements
- Professional development opportunities
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