ML Engineer
Senior Staff Machine Learning Engineer responsible for architecting, deploying, and securing enterprise‑scale AI services, integrating generative and agentic systems, and establishing standards for model serving, retrieval, auditability, and reliability.
Workiva is building a next-generation AI platform for mission-critical workflows used by some of the world’s largest and most regulated organizations. We are seeking a Senior Staff Machine Learning Engineer to define how AI is architected, deployed, and trusted across our platform at enterprise scale, where accuracy, auditability, security, and reliability matter most.
What You’ll Do
AI Platform Architecture
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
Agentic AI and Generative AI Leadership
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
Technical Leadership
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
Security, Governance, and Reliability
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
What You’ll Need
Minimum Qualifications
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
Preferred Qualifications
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,
Posted June 24, 2026