Senior Machine Learning Engineer

Veho

50d

About The Position

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

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.