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.
What You'll DoLeadership & Team Development- 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
AI Product Quality- 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
AI Evaluation Platform- 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
AI Governance & Assurance- 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
Cross-Functional Leadership- 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
Operational Excellence- 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
What You'll NeedMinimum 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 tehhnical disagreements, and build high-performance cultures
- Strong communication and cross-functional leadership skills
Preferred Qualifications- Master's degree in Computer Science, Engineering, ML, Data Science, or related field.
- Experience building or operating AI/ML evaluation, experimentation, observability, model-governance, or ML platform capabilities
- Experience evaluating RAG and agentic systems, including retrieval quality, groundedness, task completion, tool use, and safety
- Familiarity with evaluation techniques: golden datasets, statistical metrics, model-based graders, human evaluation, red teaming, A/B testing, and drift/regression detection
- Working knowledge of ML/AI lifecycle practices: dataset management, model/prompt versioning, experiment tracking, deployment, monitoring, and feedback loops
- Experience translating Responsible AI, model-risk, privacy, security, or regulatory requirements into scalable engineering controls
- Familiarity with AI risk/governance frameworks (NIST AI RMF, ISO/IEC 42001, or comparable)
- Experience with Kubernetes, microservices, CI/CD, infrastructure as code, and modern DevOps/MLOps practices
- Experience supporting enterprise software in regulated or high-assurance environments
Working Conditions- Willingness to travel up to 15% for team and corporate meetings
- Reliable internet access for remote work
How You'll Be Rewarded• Salary range in the US: $193,000.00 - $308,000.00
• A discretionary bonus typically paid annually
• Restricted Stock Units granted at time of hire
• 401(k) match and comprehensive employee benefits package
The salary range represents the low and high end of the salary range for this job in the US. Minimums and maximums may vary based on location. The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience and other relevant factors.
