Senior Machine Learning Engineering Manager – AI Quality and Governance

Workiva

Leading a multidisciplinary engineering team to drive AI quality and governance at Workiva. Establishing quality standards and overseeing the evaluation of AI systems in enterprise use.

Posted 7/30/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $193,000 - $308,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading multidisciplinary teams in software, ML, and quality engineering while establishing a comprehensive quality strategy for AI platforms. Proficient in automated quality practices, cloud-native architectures, and generative AI concepts.

Highest-signal resume keywords

Leadership In Engineering TeamsAutomated Quality PracticesGenerative AI Development LifecycleCloud-Native ArchitecturesQuality Strategy Definition

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringML EngineeringQuality EngineeringSystems DesignAutomated TestingData ExperimentationTelemetry AnalysisProduction SignalsNondeterministic Systems EvaluationQuality Criteria Definition

Soft Skills

MentoringCollaborationCommunicationTeam DevelopmentTechnical Excellence

Tools & Technologies

AWSAzureGCPSaaS PlatformsRAG PipelinesAI Evaluation PlatformsVersioning ToolsApproval WorkflowsDocumentation ToolsAuditable Evidence Systems

Industry Keywords

Generative AILLMsEmbeddingsVector SearchHybrid SearchAgentsPrompt OrchestrationCloud-Based ProductsRisk ClassificationQuality Ownership

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud Platform

About the role

Key responsibilities & impact

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
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
Build governance into the AI lifecycle through traceability, lineage, versioning, documentation, risk classification, approval workflows, and auditable evidence.
Collaborate with Product, Program Management, UX, UXR, Data Science, Security, Legal, Risk, and engineering leaders to define quality expectations and roadmaps.

Requirements

What you’ll need

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.

Benefits

Comp & perks

A discretionary bonus typically paid annually
Restricted Stock Units granted at time of hire
401(k) match and comprehensive employee benefits package