Staff Machine Learning Engineer
Remote - IA, United States
Join our team at Workiva as a Staff Machine Learning Engineer! As a pivotal member of our Machine Learning (ML) team, you'll spearhead the architecture and delivery of groundbreaking machine learning solutions across our platform. Your expertise will be instrumental in leading projects that demand innovative problem-solving, including the integration of cutting-edge Generative AI into our products.
In this role, you'll have the chance to develop robust tools, systems, and infrastructure to bolster the development, monitoring, and management of our machine learning solutions. Leveraging your engineering prowess, you'll tackle challenges related to availability and scaling, ensuring the long-term stability of our systems.
If you're passionate about pioneering the possibilities of Generative AI and want to be part of a team driving innovation at Workiva, we invite you to join us! Learn more about Workiva's Generative AI and be part of shaping the future of ML with us.
What You’ll Do
Architect and Develop Solutions
Architect and deliver cutting-edge ML solutions using MLOps and best practices, fostering creativity in project execution
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 and Lead
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
Ensure Reliability and Support
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)
What You’ll Need
Required Qualifications
Bachelor’s degree in Computer Science, Engineering or equivalent combination of education and experience
Minimum …
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10% travel
Benefits/PerksComprehensive employee benefits Comprehensive employee benefits package Discretionary bonus Employee benefits package Restricted Stock Units Salary range
Tasks- Collaborate with product teams
- Design systems
- Develop APIs
- Monitor performance
- Participate in on-call rotations
- Perform code reviews
- Provide technical leadership
- Strategic initiatives
- Training
Agile AI Amazon Web Services APIs Automation AWS Best Practices CI/CD CI/CD pipelines Cloud Services Computer Science Databases Data Pipelines Design Systems Diversity Docker Generative AI GitHub Go HTTP Infrastructure Innovation Java Kubernetes Leadership Load Testing Machine Learning Management ML MLOps Performance Tuning Problem-solving Project Execution Python Regulatory standards SaaS Security Software Engineering Strategic initiatives Support Training Web Protocols Web Services Workiva
Experience4 years
EducationBachelor's degree Computer Science Design Engineering Equivalent combination of education and experience Software Engineering
TimezonesAmerica/Anchorage America/Chicago America/Denver America/Los_Angeles America/New_York Pacific/Honolulu UTC-10 UTC-5 UTC-6 UTC-7 UTC-8 UTC-9