Application Guide

How to Apply for Senior Staff Machine Learning Engineer

at Workiva

🏢 About Workiva

Workiva is a leader in integrated ESG reporting, helping organizations streamline climate impact and compliance disclosures. Its focus on sustainability and transparency makes it a mission-driven workplace where your ML expertise directly contributes to meaningful environmental and governance outcomes.

About This Role

As Senior Staff ML Engineer, you will architect Workiva’s AI platform, integrating ML, generative AI, and agentic systems across products. Your work will shape how enterprise customers leverage AI for ESG reporting, with high impact on scalability, security, and innovation.

💡 A Day in the Life

A typical day might start with a stand-up with your AI platform team to discuss architecture decisions, followed by deep-dive design sessions for a new agentic workflow. You might review a proposal for integrating a new LLM, then collaborate with product and security teams to ensure compliance and scalability—all while mentoring engineers on best practices.

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Interview Prep

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🎯 Who Workiva Is Looking For

  • Has 10+ years of software engineering, with deep experience in large-scale SaaS platforms and production AI/ML systems (5+ years).
  • Expert in Python and at least one other production language (Java, Go, Scala, C++), with strong system design skills.
  • Specializes in RAG, agentic orchestration, multi-agent coordination, and knowledge systems at scale.
  • Demonstrates leadership in secure AI platform design, including authorization, runtime isolation, governance, and compliance.
  • Stays current with emerging AI technologies and can establish evaluation frameworks for generative AI quality.

📝 Tips for Applying to Workiva

1

Highlight specific projects where you architected and deployed large-scale agentic systems or RAG pipelines in production.

2

Tailor your resume to emphasize experience with multi-agent coordination, orchestration frameworks (e.g., LangGraph, CrewAI), and memory systems.

3

Showcase your work on AI security and governance—mention specific frameworks or compliance standards you've implemented (e.g., SOC 2, ISO 27001).

4

Quantify impact: include metrics like latency improvements, throughput, cost savings, or accuracy gains from your AI systems.

5

Mention any experience with ESG or sustainability domains, even if tangential, to demonstrate alignment with Workiva’s mission.

✉️ What to Emphasize in Your Cover Letter

['Your passion for applying AI to solve real-world sustainability challenges and how your expertise aligns with Workiva’s ESG focus.', 'Concrete examples of leading the architecture of enterprise AI platforms, especially agentic systems and RAG at scale.', 'Your approach to secure AI design—how you balance innovation with governance, auditability, and compliance.', 'Your technical leadership style and how you mentor teams while owning critical platform decisions.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Review Workiva’s ESG reporting platform and its key features (e.g., carbon accounting, compliance dashboards).
  • → Read recent blog posts or press releases about Workiva’s AI initiatives and partnerships.
  • → Understand the regulatory landscape for ESG reporting (e.g., SEC climate rules, CSRD) and how AI can address compliance.
  • → Familiarize yourself with Workiva’s engineering culture and remote-first practices via their careers page or employee reviews.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Design an agentic system for ESG report generation: multi-agent orchestration, data retrieval, quality checks.
2 How would you evaluate and ensure the quality of generative AI outputs in a compliance-sensitive context?
3 Describe a time you improved a production ML system’s latency or reliability—what trade-offs did you make?
4 How do you approach runtime isolation and authorization in a multi-tenant AI platform?
5 Discuss your experience with RAG: chunking strategies, embedding models, and retrieval optimization for large-scale knowledge bases.
Practice Interview Questions →

⚠️ Common Mistakes to Avoid

  • Being too generic about AI experience—avoid vague statements like 'I built ML models'; focus on platform-level architecture and production impact.
  • Neglecting security and governance: this role explicitly requires secure design, so don’t skip discussing authorization, isolation, or compliance.
  • Overlooking the mission: failing to connect your work to ESG or sustainability can signal misalignment with Workiva’s core values.

📅 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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