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Senior Staff Machine Learning Engineer

Meta· Machine Learning
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Tel AvivFull-timePosted 4d ago

About the role

Senior staff ML Engineer to lead the Product Recognition and Intelligence stack at Meta, tackling hardest ML systems challenges in training, serving, and evaluation at global scale, and shaping architecture and standards across the organization.

Responsibilities

  • Identify and solve the most complex machine learning systems problems across the organization, including model training efficiency, large-scale inference optimisation, and cross-system ML pipeline reliability
  • Define and own the technical architecture for critical ML infrastructure components, ensuring designs are extensible, maintainable, and capable of supporting the next generation of model development at Meta's scale
  • Establish engineering standards and invariants for ML systems that prevent entire categories of reliability, correctness, and performance issues across multiple teams
  • Drive cross-organizational technical strategy by assessing emerging ML technologies, evaluating trade-offs between competing approaches, and influencing roadmap decisions with leadership and cross-functional partners
  • Lead the design and delivery of org-wide ML platform initiatives, coordinating across engineering teams, research, product, and data science to align on priorities and drive execution through ambiguity
  • Develop new metrics and data-driven frameworks for evaluating ML system health, model quality, and infrastructure efficiency, and connecting them to organization-level goals
  • Mentor and coach engineers across the organization on ML system design, debugging complex models and infrastructure failures, and developing scalable solutions to recurring technical challenges
  • Collaborate with privacy, security, and compliance teams to design ML systems that uphold user safety, meet regulatory requirements, and set the standard for responsible AI practices within the business domain
  • Identify and eliminate engineering bottlenecks through improved tooling, automation, and process improvements that make ML development faster and more reliable across the organization
  • Contribute to recruiting efforts by building relationships with candidates who bring critical ML systems expertise, and serve as a technical reference point for evaluating engineering talent at the most senior levels

Qualifications

  • 12+ years of experience designing, building, and operating large-scale machine learning systems in production environments
  • Experience architecting ML infrastructure components such as distributed training pipelines, model serving systems, feature stores, or ML evaluation frameworks at a significant scale
  • Experience in identifying and resolving systemic performance, reliability, or correctness issues that span multiple ML systems or abstraction layers
  • Experience influencing technical direction across multiple engineering teams through written proposals, design reviews, and cross-functional stakeholder alignment
  • Experience defining engineering standards, architectural patterns, or platform capabilities that have been adopted broadly across an organization

Skills

Required:Machine Learning Systems DesignDistributed TrainingModel ServingSystem Reliability
Nice to have:Feature Stores
Last updated Oct 23, 2026. Interested? Apply on the company site.

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