Application Guide

How to Apply for Manager of Machine Learning - AI Modeling and Operation

at Workiva

🏢 About Workiva

Workiva is a leading provider of cloud-based integrated reporting solutions, with a strong focus on ESG (Environmental, Social, and Governance) reporting. The company is dedicated to streamlining complex reporting processes for transparent climate impact and compliance, making it a purpose-driven organization at the forefront of sustainability and AI innovation.

About This Role

As the Manager of Machine Learning - AI Modeling and Operation, you will lead the team responsible for the end-to-end ML infrastructure, model operations, and AI quality. This role is pivotal in ensuring that all AI features at Workiva are reliable, observable, and deployable, directly impacting the company's ability to deliver trustworthy AI solutions to enterprise clients.

💡 A Day in the Life

A typical day might involve leading stand-ups with your ML infrastructure team, reviewing model performance metrics and alerts, and collaborating with data scientists to ensure smooth model deployments. You might also engage in incident response if an AI service experiences issues, and work with product teams to align on new feature requirements and reliability expectations.

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Interview Prep

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🎯 Who Workiva Is Looking For

  • Proven experience in leading ML infrastructure and MLOps teams, with a strong background in building and maintaining CI/CD pipelines for ML models.
  • Expertise in model lifecycle management, including model registry, deployment, monitoring, and guardrails, with hands-on experience in observability tools (e.g., Prometheus, Grafana, Datadog) and SLO/SLI establishment.
  • Deep understanding of AI/ML technologies, including generative AI, and experience with model routing across multiple providers (e.g., OpenAI, Anthropic, etc.).
  • Strong leadership and communication skills, with the ability to drive incident response and ensure operational excellence in a remote-first environment.

📝 Tips for Applying to Workiva

1

Highlight specific examples of leading ML infrastructure projects where you've established SLOs/SLIs and improved reliability and observability.

2

Showcase your experience with CI/CD for ML, including automated testing and evaluation frameworks, and mention any tools like MLflow, Kubeflow, or SageMaker.

3

Emphasize your familiarity with generative AI and model routing across frontier providers, as this is a key aspect of the role.

4

Demonstrate your leadership in incident management and how you've fostered a culture of reliability within your team.

5

Tailor your resume and cover letter to align with Workiva's mission of ESG reporting and how your work in AI/ML contributes to transparent and compliant reporting.

✉️ What to Emphasize in Your Cover Letter

['Express your passion for building reliable AI infrastructure that powers enterprise features at scale.', 'Highlight your experience in leading teams and driving operational excellence in ML environments.', 'Mention your knowledge of ESG reporting and how AI can enhance transparency and compliance in this domain.', "Convey your alignment with Workiva's values and mission, and your excitement about contributing to a purpose-driven company."]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Review Workiva's ESG reporting solutions and how they leverage AI to streamline reporting for clients.
  • → Watch the provided YouTube video on Workiva's Generative AI to understand their current AI initiatives.
  • → Explore Workiva's engineering blog or tech talks to gain insights into their tech stack and engineering culture.
  • → Research Workiva's company culture and values, particularly their commitment to innovation and sustainability.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 How do you approach designing a CI/CD pipeline for ML models, and what tools have you used?
2 Describe a time when you had to troubleshoot a production AI service outage. What steps did you take?
3 How would you establish SLOs and SLIs for AI services, and how do you prioritize reliability vs. feature velocity?
4 What is your experience with model monitoring and drift detection? How do you implement these in production?
5 How do you stay current with the latest advancements in generative AI, and how would you evaluate and integrate new model providers?
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⚠️ Common Mistakes to Avoid

  • Avoid focusing solely on model development without emphasizing your experience in ML operations and infrastructure.
  • Do not overlook the importance of observability, monitoring, and SLOs in your application - these are critical to the role.
  • Avoid generic leadership examples that don't specifically relate to ML or AI - ensure your examples are relevant to the role's responsibilities.

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Workiva!

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