Machine Learning Engineer

ArcBest•Fort Smith, AR

About The Position

The Machine Learning (ML) Engineer designs, develops, installs, optimizes, and maintains the machine learning components of our cognitive systems. This position evaluates machine learning processes, performs statistical analysis to resolve data set problems, and enhances the accuracy of our AI software's predictive automation capabilities. The Machine Learning Engineer also builds high-quality, innovative, and fully performing models in compliance with coding standards and technical design and is responsible for development, writing code, troubleshooting, and documenting functionality.

Requirements

Education: Bachelor's Degree in computer science, data science, mathematics, or related field, preferred Master's Degree in computational linguistics, data analytics, or related field, preferred
Experience: Proven experience as a Machine Learning Engineer or similar role, preferred
Computer Skills: Proficiency with Python code writing, MLOps, and deployment pipelines; working knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture; basic proficiency with C#, Azure DevOps, and Agile methodology a plus.
Proficient in Microsoft Office Suite.
Competencies: Active Learning Estimating Machine Learning Producing Results Software Development Stakeholder Focus
Other Details Work Hours: Generally, 8:00 am - 5:00 pm with occasional irregular hours depending on workload. This role is subject to 24 hour on-call responsibilities as needed.
Travel Requirements: Minimal (0-25%)
Compensation: This is a salary position paid biweekly.

Responsibilities

Integrate ML components into a fully functional software system.
Understand the landscape of ML infrastructure and ML systems required to support Cognitive Engagement technologies, both current and emerging.
Develop, monitor, and maintain the ML models required to support Cognitive Engagement products throughout ArcBest.
Tailor and deploy ML tools, processes, and metrics.
Test solutions thoroughly to ensure reliable functionality prior to implementation.
Serve as a subject matter expert on machine learning.
Comply with project plans and industry standards.
Troubleshoot Cognitive Engagement system failures or performance issues, including live site support and on-call rotation.
Execute full lifecycle model development using MLOps.
Write well designed, testable, efficient code.
Develop model verification plans and quality assurance procedures.
Document and maintain model functionality.
Produce specifications and determine operational feasibility of ML models.
Other duties and projects, as assigned.