Machine Learning Engineer

XPO; Boston (MA); Hybrid; USD 100,000 - 120,000; Full time

Benefits offered by this job

Full health insurance

PTO 15 days

9 paid company holidays

401(k) with company match

Education assistance

Job summary

XPO in Boston, MA is seeking a Machine Learning Engineer to build and maintain ML data prep and validation tooling and ML infrastructure for training, evaluation, deployment, and MLOps, including CI/CD, monitoring and feedback loops.

You'll collaborate with data scientists and data engineers to productionize models and ensure reliable data pipelines, contributing to shared MLOps tooling across the AI/ML organization.

Qualifications

• Hands-on experience building data pipelines and ML infrastructure.
• Proficiency in Python and SQL for data and model workflows.
• Experience with cloud data/ML platforms (AWS/GCP/BigQuery).
• Collaborative, cross-functional work across data science and data engineering.
• 3+ years building ML infrastructure for training, evaluation, and deployment.

Responsibilities

• Build and maintain data prep and validation tooling for ML inputs.
• Design ML infrastructure for training, evaluation, and deployment.
• Develop CI/CD pipelines and automated testing for ML models.
• Implement model monitoring, drift detection, and feedback loops.
• Collaborate to productionize models and streamline experimentation-to-production.

Skills

Data prep tooling

ML infrastructure

CI/CD pipelines

Model monitoring

Drift detection

Feedback loops

Productionize models

Data pipelines

MLOps tooling

Education

Bachelor's degree in CS or related

Master's degree in CS or related

Tools

Python

SQL

AWS

GCP

BigQuery

Docker

Kubernetes

Job description

As an Machine Learning Engineer at XPO in Boston, MA (hybrid), you’ll help strengthen the foundation behind reliable ML delivery. The role focuses on building and maintaining ML data preparation and validation tooling, plus ML infrastructure that supports training, evaluation, deployment, and MLOps capabilities such as CI/CD, monitoring, and feedback loops. You’ll also collaborate closely with applied and data scientists and partner with data engineering teams to streamline the journey from experimentation to production.

What you’ll do

• Build and maintain 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
• Develop and maintain CI/CD pipelines for machine learning models, including automated testing and validation
• Implement model monitoring, drift detection, and feedback loops to track production performance
• 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

What you bring

• 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 work 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, such as AWS, GCP, or BigQuery
• Strong collaboration skills working across data science/applied science and data engineering teams
• Master’s degree in Computer Science or related field
• 3+ years 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

Technologies

Python, SQL, AWS, GCP, BigQuery, Docker, Kubernetes

Compensation and benefits

Annual salary range: $100,000 to $120,000. Actual compensation may vary based on experience and skill set. This is an incentive-based position, which may include bonuses, incentive, or commission plans.

• 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