Senior Staff Machine Learning Engineer

Meta; City of Westminster; Sur place; GBP 120 000 - 180 000; Plein temps

Résumé du poste

Meta is seeking a senior staff Machine Learning Engineer to lead the most complex ML systems challenges in training, serving, and evaluation at a global scale. You will set architecture standards and ship state-of-the-art models to production, touching billions of users.

You will influence roadmaps across teams and collaborate with research, product, and data science to align on priorities while driving execution through ambiguity.

Qualifications

• 12+ years of experience designing, building, and operating large-scale ML 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.

Responsabilités

• Identify and solve complex ML systems problems, including training efficiency, large-scale inference optimisation, and cross-system reliability.
• Define and own the architecture for critical ML infrastructure components supporting production scale.
• Set engineering standards to prevent reliability, correctness, and performance issues across teams.
• Drive cross-organizational ML platform strategy and influence roadmaps with leadership.
• Lead design and delivery of org-wide ML platform initiatives, coordinating across engineering teams, research, product, and data science.
• Develop metrics/frameworks for evaluating ML system health, model quality, and infrastructure efficiency.
• Mentor engineers on ML system design and debugging at scale.
• Collaborate with privacy, security, and compliance teams to uphold safety and responsible AI practices.
• Identify bottlenecks through tooling and automation to accelerate ML development.
• Contribute to recruiting by engaging with candidates and acting as a technical reference for senior levels.

Connaissances

ML systems

Distributed training

Model serving

Feature stores

Observability tooling

AI ethics

Experiment management

Platform migrations

Cross-functional collaboration

Talent recruitment

Description du poste

• 1. 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
• 2. 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
• 3. Establish engineering standards and invariants for ML systems that prevent entire categories of reliability, correctness, and performance issues across multiple teams
• 4. 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
• 5. 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
• 6. Develop new metrics and data-driven frameworks for evaluating ML system health, model quality, and infrastructure efficiency, and connecting them to organization-level goals
• 7. 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
• 8. 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
• 9. Identify and eliminate engineering bottlenecks through improved tooling, automation, and process improvements that make ML development faster and more reliable across the organization
• 10. 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

• 11. 12+ years of experience designing, building, and operating large-scale machine learning systems in production environments
• 12. Experience architecting ML infrastructure components such as distributed training pipelines, model serving systems, feature stores, or ML evaluation frameworks at a significant scale
• 13. Experience in identifying and resolving systemic performance, reliability, or correctness issues that span multiple ML systems or abstraction layers
• 14. Experience influencing technical direction across multiple engineering teams through written proposals, design reviews, and cross-functional stakeholder alignment
• 15. Experience defining engineering standards, architectural patterns, or platform capabilities that have been adopted broadly across an organization

Preferred Qualifications:

Preferred Qualifications:

• 16. Experience in building large-scale object recognition models
• 17. Experience collaborating with applied research teams to translate theoretical ML advances into production-grade system improvements
• 18. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• 19. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• 20. Experience developing novel approaches to ML observability, experiment management, or model lifecycle tooling that improved engineering velocity across teams
• 21. Experience leading multi-year ML platform or infrastructure migrations in complex, mature technical environments
• 22. Experience in training large language models to production systems at scale
• 23. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

Meta is reimagining shopping on Facebook, Instagram, and Muse. The vision is to make every piece of media shoppable. Behind that vision is our Product Recognition and Intelligence stack. It is the tech that looks at any photo, video, and text, and knows exactly which products are in it and how they relate to other products. We are building it to work at Meta's scale. Over 3 billion distinct users every day. Billions of items power a multibillion-dollar ad business.We are hiring a senior staff Machine Learning Engineer to lead this journey. You will tackle the hardest ML systems challenges in training, serving, and evaluation. You will set the architecture and standards that raise the bar across organizations. You will ship state-of-the‑art models to production where they touch billions.If you want to have an impact at a global scale and love building tech that actually ships, come build with us.