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Staff Machine Learning Engineer

Staff Machine Learning Engineer

Elios TalentSan Francisco, CA, United States
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Lead cutting-edge ML initiatives and platforms

Build distributed, scalable AI systems in production

Mentor and guide engineering teams in ML best practices

Job Information

Title : Staff Machine Learning Engineer

Location : Flexible / Remote

Employment Type : Full-Time

Compensation : $180,000 – $280,000

Role Summary

We are seeking a Staff Machine Learning Engineer to drive advanced ML initiatives and architect scalable platforms. You will design deep learning models, build distributed training systems, and develop robust MLOps infrastructure. This role requires both technical leadership and hands-on expertise to deliver production-ready solutions that power high-impact applications.

Key Responsibilities

  • Lead the design and implementation of deep learning models and model architectures.
  • Architect and maintain distributed ML training systems.
  • Build and scale end-to-end MLOps pipelines and infrastructure.
  • Optimize production ML workflows for efficiency, reliability, and scalability.
  • Mentor engineering teams and champion ML best practices.
  • Collaborate with cross-functional leaders to align ML initiatives with business objectives.

Requirements

  • 7+ years of professional experience in machine learning engineering.
  • Proven expertise in deep learning frameworks and model architecture.
  • Strong programming skills in Python.
  • Experience with MLflow, Kubeflow, Ray, and Spark.
  • Deep knowledge of production ML deployment and distributed systems.
  • Track record of leading teams or initiatives in machine learning.
  • About the Opportunity

    This is a senior technical leadership role for someone who thrives at the intersection of deep technical expertise and strategic influence. You’ll shape how ML platforms are built and scaled, while mentoring others and driving innovation in real-world AI applications.

    Why Join

  • Take ownership of large-scale ML platforms and strategy.
  • Competitive compensation and leadership visibility.
  • Opportunity to define and advance enterprise-level AI practices.
  • #J-18808-Ljbffr

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