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Job Description
Job Summary:
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.
Key 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
Job Summary:
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.
Key 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