Senior Machine Learning Operations Engineer

Veho•New York, NY

4d•$175,000 - $200,000

About The Position

As a Senior Machine Learning Operations Engineer, you will 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. They work in close collaboration with other Data Science team members and keep the business value at the center of their work. They have a bias for action, balancing delivering impact in the short-term while building out the long term vision. They apply their ML / MLOPS knowledge to suggest new patterns, tools, and approaches to improve the team’s models.

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
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

Nice To Haves

Experience building ML systems in Startups is a plus
Experience with DS/ML in Logistics/Supply Chain is a plus

Responsibilities

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