ZipRecruiter
Overview
We are seeking a Staff Machine Learning Engineer to lead core AI initiatives including personalization, marketplace trust, and seller tools. This role requires architecting large-scale ML systems, partnering with Data Science teams to productionize models, and mentoring junior engineers while influencing cross-functional stakeholders. Responsibilities
Technical lead for core AI initiatives with hands-on contribution Architect and deploy large-scale ML systems (batch & real-time) Partner with DS leads to productionize experimentation-ready models Drive system design across MLOps, model serving, and monitoring Mentor junior MLEs and influence cross-squad engineering culture Qualifications
Technical Skills Strong software engineering foundation with expert Python proficiency Backend and data systems experience with production model deployment Distributed systems knowledge with focus on model performance and cost optimization MLOps expertise across model serving and monitoring systems Technology Stack
ML Frameworks: PyTorch or TensorFlow Tools: Airflow, Spark, Databricks, MLFlow, Feature Store Cloud: GCP and AWS hybrid environments Infrastructure: Real-time + batch pipelines, vector DBs, scalable inference
#J-18808-Ljbffr
We are seeking a Staff Machine Learning Engineer to lead core AI initiatives including personalization, marketplace trust, and seller tools. This role requires architecting large-scale ML systems, partnering with Data Science teams to productionize models, and mentoring junior engineers while influencing cross-functional stakeholders. Responsibilities
Technical lead for core AI initiatives with hands-on contribution Architect and deploy large-scale ML systems (batch & real-time) Partner with DS leads to productionize experimentation-ready models Drive system design across MLOps, model serving, and monitoring Mentor junior MLEs and influence cross-squad engineering culture Qualifications
Technical Skills Strong software engineering foundation with expert Python proficiency Backend and data systems experience with production model deployment Distributed systems knowledge with focus on model performance and cost optimization MLOps expertise across model serving and monitoring systems Technology Stack
ML Frameworks: PyTorch or TensorFlow Tools: Airflow, Spark, Databricks, MLFlow, Feature Store Cloud: GCP and AWS hybrid environments Infrastructure: Real-time + batch pipelines, vector DBs, scalable inference
#J-18808-Ljbffr