Machine Learning Engineer

XPO is hiring a Machine Learning Engineer to help build and sustain the machine learning foundation behind training, evaluation, and deployment. This hybrid role is based in Boston, MA, with a focus on reliable data preparation and validation, ML infrastructure, and MLOps capabilities that support production performance over time.

The position includes hands-on work across the ML lifecycle, from building pipelines that ensure model-ready data to implementing CI/CD workflows for ML models. You will also help connect experimentation to deployment by partnering with applied and data scientists and collaborating closely with data engineering teams.

Role summary

• Build and maintain ML data preparation and validation tooling to support high-quality inputs for ML and optimization models
• Design and implement ML infrastructure for model training, evaluation, and deployment
• Strengthen MLOps with CI/CD, monitoring, and feedback loops to track production performance
• Contribute to shared MLOps tooling and best practices across the AI/ML organization

Responsibilities

• Develop and maintain data preparation and validation tooling to improve the quality of ML and optimization model inputs
• Create ML infrastructure that supports training, evaluation, and deployment workflows
• Build and maintain CI/CD pipelines for machine learning models, including automated testing and validation
• Implement model monitoring, drift detection, and feedback loops to measure and improve production performance
• Partner with applied and data scientists to productionize models and streamline movement from experimentation to deployment
• Work with data engineering teams to ensure data pipelines are reliable and accessible
• Support shared MLOps tooling and organizational best practices

What you’ll do on a typical day

• Build and maintain data preparation and validation tooling
• Design and implement ML infrastructure for training, evaluation, and deployment
• Maintain CI/CD pipelines for machine learning models with automated testing and validation
• Implement monitoring, drift detection, and feedback loops for production models
• Collaborate with applied and data scientists to productionize models
• Coordinate with data engineering teams to ensure dependable, accessible data pipelines
• Contribute to shared MLOps tooling and best practices

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 ML pipelines
• Proficiency in Python and SQL
• Experience with cloud data or ML platforms (examples: AWS, GCP, BigQuery)
• Strong collaboration skills partnering with data science/applied science teams and data engineering teams
• 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 ML 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

Technologies

• Python
• SQL
• AWS
• GCP
• BigQuery
• Docker
• Kubernetes

Compensation and location

• Location: Boston, MA (hybrid)
• Annual salary range: USD $100,000 - $120,000 per year
• Actual compensation may vary based on experience and skill set
• This is an incentive-based position, which may include bonuses, incentive or commission plans

Benefits

• Competitive compensation package
• Full health insurance benefits available on day one
• Life and disability insurance
• Earn up to 15 days of PTO over your first year
• 9 paid company holidays
• 401(k) option with company match
• Education assistance
• Opportunity to participate in a company incentive plan