Machine Learning Engineer - Hybrid

Quick Facts

Annual Salary Range: $100,000 to $120,000. Competitive compensation package with possible bonuses or incentive/commission plans.

Description

This Machine Learning Engineer role builds and maintains data preparation and validation tooling, designs ML infrastructure for training, evaluation, and deployment, and implements CI/CD pipelines for machine learning models. You will also implement model monitoring, drift detection, and feedback loops, partner with applied and data scientists to productionize models, and collaborate with data engineering teams to ensure reliable, accessible data pipelines.

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

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

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

Benefits

• 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