Application Guide

How to Apply for Staff Machine Learning Engineer

at Workiva

🏢 About Workiva

Workiva specializes in integrated ESG (Environmental, Social, and Governance) reporting, helping organizations achieve transparent climate impact tracking and compliance. The company stands out by focusing on sustainability reporting technology and is actively integrating Generative AI into their platform, as evidenced by their public video content. This makes Workiva particularly appealing for ML engineers interested in applying cutting-edge AI to meaningful environmental and governance challenges.

About This Role

As a Staff Machine Learning Engineer at Workiva, you'll lead the architecture and delivery of ML solutions across their platform, with a specific emphasis on integrating Generative AI into their products. This role involves developing robust MLOps tools and infrastructure to ensure high availability, scalability, and observability of ML systems. Your work will directly impact how organizations measure and report their climate impact through innovative AI-driven solutions.

💡 A Day in the Life

A typical day involves collaborating with cross-functional teams to architect ML solutions for ESG reporting features, implementing MLOps pipelines for new Generative AI capabilities, and optimizing existing ML infrastructure for better performance and observability. You'll spend time designing systems that ensure high availability while mentoring other engineers on best practices for scalable ML solutions.

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🎯 Who Workiva Is Looking For

  • Has extensive experience architecting and delivering production ML solutions using MLOps best practices
  • Possesses strong engineering skills to tackle availability and scaling challenges for long-term system stability
  • Has hands-on experience with Generative AI integration into products and can demonstrate innovative problem-solving in this area
  • Can design systems that enable rapid ML development while maintaining clear observability and monitoring capabilities

📝 Tips for Applying to Workiva

1

Watch Workiva's Generative AI video (linked in the job description) and reference specific insights in your application

2

Highlight specific examples where you've architected ML solutions using MLOps practices, not just built models

3

Demonstrate experience with both traditional ML and Generative AI, emphasizing how you've integrated AI into products

4

Showcase projects where you've solved availability and scaling challenges for ML systems in production

5

Tailor your resume to emphasize system design and infrastructure experience over just model development

✉️ What to Emphasize in Your Cover Letter

["Your experience architecting ML solutions using MLOps best practices and how it aligns with Workiva's needs", 'Specific examples of integrating Generative AI into products and the business impact achieved', 'How your engineering approach addresses availability, scaling, and long-term system stability challenges', "Why you're passionate about applying ML to ESG reporting and climate impact transparency specifically"]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Watch Workiva's Generative AI video and explore their ESG reporting platform capabilities
  • → Research Workiva's specific ESG reporting solutions and how ML could enhance them
  • → Understand the regulatory landscape for ESG reporting (SEC climate rules, EU CSRD, etc.) that Workiva addresses
  • → Look into Workiva's technology stack and recent ML/AI initiatives through their blog or press releases

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk through how you would architect an ML solution for ESG reporting data using MLOps principles
2 Discuss your experience with Generative AI integration and specific challenges you've overcome
3 How do you design systems for high availability and observability in ML production environments?
4 Describe a time you led a complex ML project from architecture to delivery
5 How would you approach scaling ML systems for a platform like Workiva's with diverse reporting needs?
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⚠️ Common Mistakes to Avoid

  • Focusing only on model development without demonstrating MLOps and system architecture experience
  • Generic AI/ML experience without specific examples of Generative AI integration into products
  • Not showing understanding of Workiva's specific domain (ESG reporting) and how ML applies to it

📅 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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