Senior AI Quality & Governance Leader for ML Platform

Workiva, Inc.; Ames (IA); Remote; USD 193,000 - 308,000; Full time

Job summary

Workiva, Inc. is seeking a Sr Machine Learning Engineering Manager to shape how we build, evaluate, release, and operate trustworthy AI at scale. You will lead a multidisciplinary team responsible for AI quality across the platform and for governance capabilities that make AI systems measurable and enterprise-ready.

You will partner with Product, Engineering, Data Science, Security, Risk, and Legal to embed quality throughout the AI lifecycle, from design to deployment and ongoing monitoring.

Qualifications

• 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 technical disagreements, and build high-performance cultures.
• Strong communication and cross-functional leadership skills.

Responsibilities

• 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.

Skills

Strong software engineering

ML engineering

Quality engineering

Leadership experience

Cloud-native

Communication skills

Education

Bachelor's degree in CS/Engineering/Data Science

Master's degree (preferred)

Tools

Kubernetes

CI/CD

ML platforms

Job description

Workiva, Inc. is seeking a Sr Machine Learning Engineering Manager to shape how we build, evaluate, release, and operate trustworthy AI at scale. You will lead a multidisciplinary team responsible for AI quality across the platform and for governance capabilities that make AI systems measurable and enterprise-ready.

You will partner with Product, Engineering, Data Science, Security, Risk, and Legal to embed quality throughout the AI lifecycle, from design to deployment and ongoing monitoring.