Senior Director of Engineering, Workiva

• Bachelor’s degree in Computer Science, Engineering, or an equivalent combination of education and experience
• 12+ years of experience in engineering roles, with significant focus in AI/ML, including leading teams of ML Engineers and Data Scientists
• Demonstrated ability to strategically align AI/ML initiatives with overall business objectives
• Excellent leadership and communication skills, with a demonstrated ability to inspire and guide teams while solving complex technical challenges
• A track record of fostering innovation and rapid prototyping while also delivering high-impact, operational solutions

Desirable

• Build and manage a high-performing team of ML Engineers and Data/ML Scientists, fostering a collaborative and innovative environment
• Develop and execute AI/ML initiatives that align with Workiva's broader goals, with a focus on both traditional supervised machine learning techniques and emerging Generative AI technologies
• Guide your team in designing new AI models and capabilities, including supervised learning models that optimize performance and generative models that expand Workiva’s platform capabilities and enhance customer solutions
• Oversee the architecture, development, and scaling of AI systems, ensuring seamless integration into the platform
• Collaborate cross-functionally with teams like product, engineering, and business leadership to shape, refine and deliver Workiva's AI/ML strategy, both in the short and long term
• Foster a culture of experimentation, innovation, and rapid prototyping, while ensuring that deployed solutions deliver real value and drive business growth
• Champion the secure and ethical use of AI within all AI/ML initiatives
• Develop and maintain tools for data ingestion, processing, feature engineering, and data management
• Create and manage tools for model performance monitoring, optimization, and distributed processing, including monitoring both supervised ML and generative models
• Monitor and ensure the health of AI models and data systems, implementing best practices for scaling AI-powered features with rapid iteration and improvement across both traditional and generative AI solutions