Machine Learning Engineer - Hybrid

XPO•Boston, MA

12d•$100,000 - $120,000•Hybrid

About The Position

XPO is a top ten global provider of transportation services, with a highly integrated network of people, technology and physical assets. We are looking for employees who like a challenge and can communicate effectively in all situations. We want to leverage your skills and years of experience to drive positive results while ensuring a bright future for yourself and XPO. If you’re looking for a growth opportunity, join us at XPO. We are proud to be an Equal Opportunity employer. Qualified applicants will receive consideration for employment without regard to race, sex, disability, veteran or other protected status. All applicants who receive a conditional offer of employment may be required to take and pass a pre-employment drug test. The above statements are not an exhaustive list of all required responsibilities, duties and skills for this job classification.

Requirements

Bachelor's degree in Computer Science, Engineering, or related field, or equivalent related work or military experience
1 year of experience in software or machine learning engineering, including hands-on experience building data pipelines, ML infrastructure, or MLOps tooling
Experience developing data preparation, validation, or quality-checking tooling for machine learning pipelines
Proficiency in Python and SQL
Experience with cloud data or ML platforms (e.g., AWS, GCP, BigQuery)
Strong collaboration skills, with experience partnering with data science/applied science teams and data engineering teams

Nice To Haves

Master's degree in Computer Science or related field
3+ years of experience building ML infrastructure for training, evaluation, and deployment at scale
Experience building and maintaining CI/CD pipelines for machine learning models
Experience with model serving and inference infrastructure (batch and real-time)
Experience implementing model monitoring, drift detection, and feedback-loop tooling
Experience with containerization and orchestration (Docker, Kubernetes)
Experience partnering with data engineering teams on data pipeline reliability and access

Responsibilities

Build and maintain data preparation and validation tooling to ensure high-quality inputs for ML and optimization models
Design and implement ML infrastructure for model training, evaluation, and deployment
Build and maintain CI/CD pipelines for machine learning models, including automated testing and validation
Implement model monitoring, drift detection, and feedback loops to track model performance in production
Partner with applied and data scientists to productionize models and streamline the path from experimentation to deployment
Collaborate with data engineering teams to ensure reliable, accessible data pipelines
Contribute to shared MLOps tooling and best practices across the AI/ML organization