Manager of Machine Learning - AI Modeling and Operation

Workiva Inc.•Remote - IA, IA

3d•$163,000 - $290,000•Remote

About The Position

Join our team at Workiva as an Manager of Machine Learning - AI Modeling and Operation! As a pivotal member of our AI/ML team, you’ll own the infrastructure that makes every AI feature at Workiva reliable, observable, and deployable. You will lead the team responsible for ML infrastructure, model operations, and AI quality at Workiva. Your team owns the systems that make AI reliable in production from model lifecycle management to evaluation frameworks, observability, and model routing across frontier providers. You'll build the operational backbone that every AI feature at Workiva depends on. Join us if you want to own the infrastructure layer that makes enterprise AI work at scale, not just build demos. Discover more about Workiva's Generative AI.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science or equivalent combination of education and experience
7+ years of total experience in software engineering and/or Machine Learning, with at least 2 years of dedicated experience as an Engineering Manager
Experience with ML pipeline orchestration tools (ClearML, Kubeflow, Airflow, or similar)
Hands-on background with Kubernetes, microservices, container orchestration, and infrastructure-as-code
Track record of improving reliability/availability metrics for production ML systems
Proven ability to manage senior individual contributors, resolve technical conflicts, and build a culture of psychological safety and high performance
Solid leadership skills in an Agile/Sprint working environment
Experience operating production ML systems in cloud environments (AWS, Azure, or GCP)

Nice To Haves

Master’s degree in Computer Science, Engineering, Data Science or equivalent combination of education and experience
Experience with core concepts of Generative AI such as RAG, Agentic frameworks, etc
Experience building model evaluation or quality measurement systems
Familiarity with cost optimization for GPU/model serving workloads
Familiarity with observability tooling (Datadog, Prometheus, Grafana)

Responsibilities

Own the ML model lifecycle: training pipelines, model registry, deployment, monitoring, and guardrails
Build and maintain CI/CD for ML - automated testing, evaluation, and promotion of models across environments
Drive observability across AI services: latency tracking, drift detection, cost monitoring, alerting
Establish SLOs/SLIs for AI services and lead incident response for ML-related production issues and maintain high service availability
Reduce complexity through simplification, automation, and thoughtful system design
Lead and grow an existing strong team of machine learning engineers
Provide hands-on coaching, performance feedback, and growth opportunities for engineers at varying experience levels
Foster a collaborative, inclusive, and high-ownership team culture grounded in trust, accountability, and continuous improvement
Partner with Intelligence pillar engineering squads (AGFW, AIEI, AIQG, Applied AI, Search) and Product teams to ensure AI services are production-ready, operationally sound, and observable
Communicate complex technical issues to both technical and non-technical audiences effectively
Manage integrations with frontier model providers (AWS Bedrock, Azure OpenAI, Google) including model routing, load balancing, and fallback strategies
Drive AI analytics dashboards that give leadership and product visibility into platform health and usage
Drive improvements in latency, service availability, developer experience, and integration usability across internal and external interfaces
Guide architectural decisions to ensure platform scalability, reliability, and alignment with Workiva’s long-term technical vision