Staff Machine Learning Engineer

Role Overview

• Architect and deliver cutting-edge ML solutions using MLOps and best practices
• Design systems to enable rapid ML development, high availability, and clear observability
• Develop tools, systems, and automation to support ML solutions, ensuring efficiency, scalability, and rapid development
• Collaborate closely with product teams to develop APIs, maintain ML infrastructure, and integrate machine learning features into products
• Provide technical leadership, mentor less experienced ML engineers and scientists, and define team best practices and processes
• Lead in the ML space by introducing new technologies and techniques, and applying them to Workiva's strategic initiatives
• Communicate complex technical issues to both technical and non-technical audiences effectively
• Collaborate with software, data architects, and product managers to design complete software products that meet a broad range of customer needs and requirements
• Deliver, update, and maintain machine learning infrastructure to meet evolving needs
• Host ML models to product teams, monitor performance, and provide necessary support
• Write automated tests (unit, integration, functional, etc.) with ML solutions in mind to ensure robustness and reliability
• Debug and troubleshoot components across multiple service and application contexts, engaging with support teams to triage and resolve production issues
• Participate in on-call rotations, providing 24x7 support for all of Workiva’s SaaS hosted environments
• Perform Code Reviews within your group’s products, components, and solutions, involving external stakeholders (e.g., Security, Architecture)

Requirements

• Bachelor’s degree in Computer Science, Engineering or equivalent combination of education and experience
• Minimum of 4 years in ML engineering or related software engineering experience
• Proficiency in ML development cycles and toolsets
• Familiarity with Generative AI
• Strong technical leadership skills in an Agile/Sprint working environment
• Experience building model deployment and data pipelines and/or CI/CD pipelines and infrastructure
• Proficiency in Python, GO, Java, or relevant languages
• Experience in Github, Docker, Kubernetes, and cloud services
• Proven experience working with product teams to integrate machine learning features into the product
• Experience with commercial databases and HTTP/web protocols
• Knowledge of systems performance tuning and load testing, and production-level testing best practices
• Experience with Github or equivalent source control systems
• Experience with Amazon Web Services (AWS) or other cloud service providers
• Ability to prioritize projects effectively and optimize system performance.

Tech Stack

• AWS
• Cloud
• Docker
• Java
• Kubernetes
• Python

Benefits

• A discretionary bonus typically paid annually
• Restricted Stock Units granted at time of hire
• 401(k) match and comprehensive employee benefits package