Senior Machine Learning Operations Engineer

About the role

This role is embedded in a team of talented data scientists and software engineers to create sophisticated models that improve Veho's logistics network and user experiences. It bridges between ML platform work and building on top of Veho's platforms to create new models. The Senior Machine Learning Operations Engineer will create the infrastructure and tooling necessary to deploy, monitor, and scale machine learning models in production.

Responsibilities

• Create reliable, efficient, and scalable infrastructure for AI/ML capabilities
• Create robust data pipelines to feed analyses and models
• Enable forecasting, network orchestration, and live pricing systems
• Ensure data quality and data integrity through best practices in data integration
• Build out robust feature stores, model orchestration tooling, experimentation tooling, and model performance monitoring
• Create standards and templates for model development and deployment across all Data Science teams

Requirements

• Bachelor’s Degree plus at least 3 years of experience in machine learning engineering, or Master’s Degree plus at least 2 years in machine learning engineering
• Experience developing and optimizing MLOps pipelines for speed, reliability, and observability
• Experience utilizing statistical modeling or machine learning techniques to solve business problems
• Strong proficiency in Python and SQL
• Hands-on experience with open-source languages and tooling for large-scale ML (e.g., Ray, Flink, Feast)
• Experience working with Data Warehouses (e.g., Redshift, Databricks, Snowflake)
• Experience building ML systems in startups is a plus
• Experience with DS/ML in logistics/supply chain is a plus

Qualifications

• Expertise in machine learning operations engineering
• Works in close collaboration with the data science team and focuses on delivering business value
• Has a bias for action, balancing short-term impact with long-term vision

Skills

• Python
• SQL
• Open-source languages and tooling for large-scale ML (e.g., Ray, Flink, Feast)
• Cloud-based (AWS preferred) data engineering and data science tools

Benefits

• Generous equity and career growth opportunities
• Mission-driven company focused on driving commerce forward with a customer-centric delivery and returns experience

Pay

• Competitive compensation package

Schedule

• Full-time position