Manager, Machine Learning – AI Modeling and Operation

This is a fully remote position, open to applicants in United States.

📋 Description

• Take ownership of the ML model lifecycle, encompassing training pipelines, model registry, deployment, monitoring, and implementing guardrails.

• Develop and sustain CI/CD for ML, which includes automated testing, evaluation, and model promotion across different environments.

• Enhance observability across AI services by tracking latency, detecting drift, monitoring costs, and setting up alerts.

• Define SLOs/SLIs for AI services and spearhead incident response for ML-related production challenges.

• Simplify processes through automation, thoughtful system design, and reducing complexity.

• Lead and expand an existing team of machine learning engineers.

• Offer hands-on coaching, performance assessments, and opportunities for growth.

• Cultivate a collaborative, inclusive, and high-ownership team culture.

• Collaborate with engineering squads and Product teams to ensure AI services are ready for production, operationally sound, and easily observable.

• Convey complex technical issues effectively to both technical and non-technical audiences.

• Manage integrations with leading model providers, addressing model routing, load balancing, and fallback strategies.

• Drive the development of AI analytics dashboards for platform health and usage insights.

• Enhance latency, service availability, developer experience, and integration usability.

• Guide architectural decisions to ensure platform scalability, reliability, and alignment with Workiva’s technical vision.

⛳️ Requirements

• Bachelor's degree in Computer Science, Engineering, Data Science, or a relevant combination of education and experience.

• Over 7 years of overall experience in software engineering and/or Machine Learning.

• Minimum of 2 years of focused experience as an Engineering Manager.

• Familiarity with ML pipeline orchestration tools like ClearML, Kubeflow, Airflow, or similar platforms.

• Practical experience with Kubernetes, microservices, container orchestration, and infrastructure-as-code.

• Proven track record of enhancing reliability and availability metrics for production ML systems.

• Ability to manage senior individual contributors, resolve technical disputes, and foster psychological safety and high performance.

• Strong leadership capabilities within an Agile/Sprint working environment.

• Experience managing production ML systems in cloud environments such as AWS, Azure, or GCP.

• Willingness to travel up to 15% for team and corporate meetings.

• Dependable internet access for remote working opportunities.

• Preferred: Master’s degree in Computer Science, Engineering, Data Science, or equivalent experience.

• Preferred: Experience with Generative AI concepts, including RAG and Agentic frameworks.

• Preferred: Background in building model evaluation or quality measurement systems.

• Preferred: Knowledge of cost optimization for GPU/model serving workloads.

• Preferred: Familiarity with observability tools such as Datadog, Prometheus, or Grafana.

🏝️ Benefits

• A discretionary bonus typically awarded annually.

• Restricted Stock Units granted upon hiring.

• 401(k) matching program.

• Comprehensive employee benefits package.

• Opportunity to work remotely from anywhere within the country of employment.

• Travel up to 15% for team and corporate meetings.