Senior Machine Learning Engineering Manager, Workiva

• Bachelor's degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience)
• 10+ years in software engineering, ML engineering, quality engineering, or related roles, including 4+ years leading an engineering team
• Strong software engineering and systems-design fundamentals, with experience delivering and operating production SaaS or platform capabilities
• Demonstrated experience establishing automated quality practices for distributed, cloud-based products
• Practical understanding of the generative AI development lifecycle and challenges of evaluating nondeterministic systems
• Experience with generative AI concepts: LLMs, RAG, embeddings, vector/hybrid search, agents, tool use, and prompt orchestration
• Experience defining measurable quality criteria using data, experimentation, telemetry, and production signals
• Experience with cloud-native architectures on AWS, Azure, or GCP
• Proven ability to lead senior individual contributors, navigate tehhnical disagreements, and build high-performance cultures
• Strong communication and cross-functional leadership skills
• Willingness to travel up to 15% for team and corporate meetings
• Reliable internet access for remote work

Desirable

• Join Workiva as a Sr Machine Learning Engineering Manager - AI Quality and Governance and help establish how we build, evaluate, release, and operate trustworthy AI products at scale
• You will lead a multidisciplinary team of software, machine learning, and quality engineers responsible for two connected missions: advancing end-to-end quality across Workiva's AI platform and products, and building shared evaluation and governance capabilities that make our AI systems measurable, observable, reliable, and ready for enterprise use
• Your team's scope spans generative AI and agentic products, including AI platform services, agent frameworks and runtimes, conversational experiences, and RAG/knowledge systems
• You will partner across Product, Engineering, Data Science, Security, Risk, and Legal to establish practical quality standards and embed evaluation and governance throughout the AI development lifecycle
• Lead, mentor, and develop a multidisciplinary team of software, ML, and quality engineers
• Build a culture of technical excellence, quality ownership, experimentation, and continuous improvement
• Establish clear team priorities while balancing platform investments, product needs, and enterprise risk
• Recruit engineers with complementary expertise across software quality, ML evaluation, platform engineering, and governance automation
• Define and drive a comprehensive quality strategy for Workiva's AI platform and products, spanning unit, integration, end-to-end, performance, resilience, security, and production testing
• Establish measurable quality bars, release-readiness criteria, and automated quality gates for AI and agentic capabilities
• Advance testing approaches for nondeterministic systems, including RAG pipelines, agents, prompts, models, tools, and multi-step workflows
• Detect regressions, model or data drift, unsafe behavior, and degraded customer experiences before and after release
• Lead architecture and delivery of a scalable, self-service evaluation platform for generative AI, RAG, and agentic systems
• Enable teams to create, manage, version, and reuse evaluation datasets, golden test sets, task-specific metrics, graders, and benchmarks
• Support deterministic checks, statistical metrics, model-based graders, human evaluation, adversarial testing, and domain-expert review
• Build capabilities for offline evaluation, pre-release regression testing, online experimentation, production sampling, and continuous evaluation
• Ensure evaluation results are reproducible, explainable, actionable, and integrated into developer workflows, CI/CD pipelines, and operational dashboards
• Translate Workiva's Responsible AI principles into practical engineering controls and platform capabilities
• Build governance into the AI lifecycle through traceability, lineage, versioning, documentation, risk classification, approval workflows, and auditable evidence
• Partner with Security, Legal, Privacy, Compliance, and Risk teams to define controls that support enterprise and regulated use cases
• Enable inventories and traceability across models, prompts, datasets, evaluations, tools, knowledge sources, and deployed AI features
• Collaborate with Product, Program Management, UX, UXR, Data Science, Security, Legal, Risk, and engineering leaders to define quality expectations and roadmaps
• Influence engineering teams across Workiva to adopt shared evaluation standards, testing practices, observability, and release controls
• Communicate complex technical tradeoffs, quality signals, and risk findings clearly to technical and non-technical audiences
• Ensure the evaluation and governance platform is secure, scalable, reliable, observable, and cost-effective
• Define service-level objectives and meaningful operational and quality metrics
• Champion production readiness, incident response, root-cause analysis, and continuous operational improvement