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Staff MLE

Staff MLE

HarnhamMundelein, IL, US
1 day ago
Job type
  • Full-time
Job description

Job Description

Staff Machine Learning Engineer

SF Bay Area : Hybrid (3 days onsite / wk)

$270,000 - $325,000 Base + Equity

A leading commerce marketplace with 130M+ users and billions of daily events is hiring a Staff Machine Learning Engineer . Their marketplace connects buyers and sellers through personalized, trustworthy, and engaging experiences. With a strong engineering culture and a focus on applied AI, their team is building next-gen features to shape how users discover, connect, and transact.

Role

We’re looking for a Staff MLE to take technical leadership of a core AI initiative. This is a high-impact, hands-on role where you’ll architect and deploy large-scale ML systems, drive end-to-end model productionization, and influence engineering culture across squads. You’ll partner closely with data scientists and product teams to deliver real-time, intelligent features that delight users and scale globally.

Responsibilities

  • Lead architecture and system design for large-scale ML systems (batch & real-time)
  • Deploy experimentation-ready models into production with DS partners
  • Build robust MLOps pipelines for serving, monitoring, and optimizing models
  • Develop real-time inference systems, including vector database integrations
  • Mentor junior engineers and foster a high-performance technical culture
  • Tackle challenging problems in personalization, fraud prevention, search, and GenAI tools

Requirements

  • Strong software engineering foundation with Python and backend / data systems
  • Proven experience deploying ML models in production at scale
  • Expertise in distributed systems, performance tuning, and cost optimization
  • Proficiency with PyTorch or TensorFlow, Airflow, Spark, Databricks, MLFlow
  • Experience with real-time and batch pipelines, feature stores, and scalable inference
  • Marketplace, e-commerce, or large-scale content platform experience
  • Familiarity with GenAI / LLM ops, real-time personalization, or fraud detection
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