Own the full ML lifecycle including feature pipelines, training workflows, model deployment, inference services, monitoring, and retraining
Design and build reliable data and feature pipelines
Build and optimize machine learning models including regression, classification, ranking, and recommender systems
Implement and manage batch scoring pipelines and online inference services
Partner with data scientists to operationalize models
Collaborate with software engineers for integration into production services
Establish observability for ML systems
Design systems for rapid experimentation and safe rollout of new models
Document architecture, establish best practices, and mentor teammates

Requirements

5+ years of professional experience in machine learning engineering, software engineering, data engineering, or similar roles
Fluency with Python and SQL
Proven experience designing, building, and operating data pipelines at scale
Hands-on experience deploying and maintaining machine learning models in production environments
Experience working in cloud environments, especially AWS
Familiarity with orchestration and data modeling tools such as Airflow, dbt, or similar systems
Experience building ranking, recommendation, or decisioning systems is a plus.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningdata pipelinesmodel deploymentinference servicesregressionclassificationranking systemsrecommender systemsPythonSQL

Soft Skills

collaborationmentoringdocumentationbest practices