Workiva
Sr Machine Learning Engineering Manager - AI Quality and Governance
USA - Remote · Senior
Sponsorship not specified$193k-$308kDetected 1 day ago
AWSAzureKubernetesCI/CDDevOpsPlatform EngineeringMachine LearningData ScienceLLMsRAGAgentic AIMLOpsA/B TestingIncident ResponseManual TestingLeadershipCommunication
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37Unrated
Cap-exempt (no lottery)0
Sponsors this role0
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70
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About the role
- 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.
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
- 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
- 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
Requirements
- 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
- Experience defining measurable quality criteria using data, experimentation, telemetry, and production signals
- Experience with cloud-native architectures on AWS, Azure, or GCP.
- With an unbroken path from source to output, leaders gain confidence in their numbers, visibility into current and emerging risks, and the ability to move with speed and precision in a constantly changing world.
Nice to have
- Master's degree in Computer Science, Engineering, ML, Data Science, or related field.
- Experience evaluating RAG and agentic systems, including retrieval quality, groundedness, task completion, tool use, and safety
- 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
- E xperience supporting enterprise software in regulated or high-assurance environments
- Willingness to travel up to 15% for team and corporate meetings
- 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
Compensation
- ✅ Salary range in the US: $193,000.00 - $308,000.00 ✅ A discretionary bonus typically paid annually
Benefits
- ✅ Salary range in the US: $193,000.00 - $308,000.00 ✅ A discretionary bonus typically paid annually
Visa & Work Authorization
- ability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other protected characteristic
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This listing is sourced directly from Workiva's careers page and normalized into a canonical job model.