Senior Machine Learning Operations Engineer

As a Senior Machine Learning Operations Engineer you'll be embedded in a team of talented data scientists and software engineers to create sophisticated models that answer hard questions centered around improving our logistics network and user experiences. This role bridges between ML platform work and building on top of our platforms to create new models. You'll create the infrastructure and tooling necessary to deploy, monitor, and scale our machine learning models in production. In close collaboration with data scientists you'll own our production models, ensuring optimal performance and responding to production incidents.

A great candidate:

Is an expert in their craft, creating high quality ML infrastructure and delivering impactful machine learning models to our stakeholders.

Works in close collaboration with the other Data Science team members and keeps the business value at the center of their work. Has a bias for action, balancing delivering impact in the short-term while building out the long term vision.

Applies their ML / MLOPS knowledge to suggest new patterns, tools, approaches to improve the team's models

What you'll do:

• Build reliable, efficient, and scalable infrastructure for our 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, model performance monitoring.
• Create standards and templates for model development and deployment across all Data Science teams.

• 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
• This experience should include:
• Developing and optimizing MLOps pipelines for speed, reliability, and observability.
• 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).
• Working with Data Warehouses (e.g., Redshift, Databricks, Snowflake).
• Utilizing cloud-based (AWS Preferred) data engineering and data science tools.
• Experience building ML systems in Startups is a plus
• Experience with DS/ML in Logistics/Supply Chain is a plus.

2.62.6 out of 5 stars Philadelphia, PA $175,000 - $200,000 a year - Full-time, Pulled from the full job description

• Opportunities for advancement

Veho's mission is to power the future of commerce by making shopping, shipping and returns seamless for everyone. We are building a modern, end-to-end logistics infrastructure designed entirely for the ever-evolving needs of ecommerce brands and everyday consumers. Powered by next-generation technology and a vertically integrated supply chain, Veho gives brands and their customers unprecedented control over their deliveries and removes the pain from the ecommerce post-purchase experience. We make delivery the 'extension of the brand' and leverage it to create deeper loyalty and trust between brands and their customers, driving customer retention and lifetime value. Our rapidly growing client list includes leading consumer brands like Hello Fresh, Zara, Macy's, Sephora, and more. To truly build an iconic company, we strongly believe that our people and values must be aligned with our mission. As such, we take pride in our championship team, merit-based culture. We seek team players who want to compete, win, make an impact and build a legacy, and we reward performance and impact players with generous equity and incredible career growth opportunities., Veho is a growth company that looks for team members to grow with it. No matter the location, or the role, every Veho teammate shares one galvanizing mission: driving commerce forward with a customer-centric delivery and returns experience that's built for the modern era. We are deeply value-driven (Team Up, Drive Impact, Take Ownership, Solve Bigger, Obsess Over Experience, Make Today Count) and care tremendously about investing in our high-performers.