3 days ago
Tel Aviv-Yafo, Israel or London, United KingdomStaff+
Responsibilities
- Solve complex machine learning systems problems involving training efficiency, large-scale inference optimization, and ML pipeline reliability.
- Define technical architecture for critical ML infrastructure components that support scalable model development.
- Establish engineering standards and architectural invariants for reliability, correctness, and performance across teams.
- Set cross-organizational technical strategy and influence roadmap decisions through technology evaluation and stakeholder alignment.
- Lead the design and delivery of organization-wide ML platform initiatives across engineering, research, product, and data science.
- Develop metrics and data-driven frameworks for ML system health, model quality, and infrastructure efficiency.
- Mentor and coach engineers on ML system design, model and infrastructure debugging, and scalable solutions.
- Collaborate with privacy, security, and compliance teams on safe, compliant, and responsible AI systems.
- Improve ML development through tooling, automation, and process improvements.
- Support recruiting and evaluate senior engineering talent with critical ML systems expertise.
Requirements
- 12+ years of experience designing, building, and operating large-scale machine learning systems in production environments.
- Experience architecting distributed training pipelines, model serving systems, feature stores, or ML evaluation frameworks at significant scale.
- Experience resolving systemic performance, reliability, or correctness issues across multiple ML systems or abstraction layers.
- Experience influencing technical direction across multiple engineering teams through proposals, design reviews, and stakeholder alignment.
- Experience defining engineering standards, architectural patterns, or platform capabilities adopted broadly across an organization.
- Preferred experience building large-scale object recognition models and translating applied research advances into production systems.
- Preferred experience with AI skill development such as prompt/context engineering and agent orchestration.
- Preferred experience using AI tools to improve workflows and deliver measurable efficiency or quality gains.
- Preferred experience developing ML observability, experiment management, or model lifecycle tooling.
- Preferred experience leading multi-year ML platform or infrastructure migrations.
- Preferred experience training large language models for production systems at scale.
- Preferred experience implementing responsible and ethical AI practices, including risk assessment, bias mitigation, and quality reviews.
Categories
About Meta
Meta builds social platforms and communication apps—including Facebook, Instagram, WhatsApp, and Messenger—and develops AR/VR hardware and software such as Quest to power immersive computing. It monetizes primarily through advertising tools for businesses, with additional revenue from devices and services, and operates a massive global infrastructure. Founded in 2004 and headquartered in Menlo Park, California, Meta Platforms, Inc. is a public company traded on Nasdaq under the ticker META.