What you'll need to succeed as a Machine Learning Engineer at XPOMinimum qualifications:
- 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
Preferred qualifications:
- 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
About the Machine Learning Engineer jobPay, benefits and more:
- 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
What you'll do on a typical day:
- 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
Annual Salary Range: $100,000 to $120,000 Actual compensation may vary due to factors such as experience and skill set. This is an incentive-based position, which may include bonuses, incentive or commission plans.
