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 closely with product teams to develop APIs, maintain ML infrastructure, and integrate machine learning features into products
Mentor less experienced ML engineers and scientists, and define team best practices and processes
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 2 years in ML engineering or related software engineering experience
Proficiency in ML development cycles and toolsets
Experience with Generative AI
Experience working 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, with 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.

Benefits

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

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningMLOpsAPIsmodel deploymentdata pipelinesCI/CD pipelinesPythonGOJavasystems performance tuning

Soft Skills

mentoringcommunicationcollaborationproblem-solvingproject prioritization

Certifications

Bachelor’s degree in Computer ScienceBachelor’s degree in Engineering