Manager of Machine Learning

Who you are

• 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)
• 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)

What the job involves

• 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
• 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