Senior Staff Machine Learning Engineer

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Workiva

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2008

☁️ SaaS

💸 Finance

📋 Compliance

SaaS • Finance • Compliance

Workiva is a cloud-based, AI-powered platform that unifies financial reporting, risk and compliance management, and sustainability reporting. It enables teams to connect data across systems, collaborate in real time, automate reporting (including XBRL tagging), and maintain auditability and traceability for regulated disclosures and assurance processes. Workiva serves large enterprises and finance, legal, audit, and sustainability teams to accelerate reporting, improve accuracy, and reduce risk.

📋 Description

• 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