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About the Role
Meta is reimagining shopping on Facebook, Instagram, and Muse with the vision to make every piece of media shoppable. The Product Recognition and Intelligence stack is the technology that identifies products in media and their relationships. We are building this at Meta's scale, serving over 3 billion users daily and powering a multibillion-dollar ad business. We are hiring a Senior Staff Machine Learning Engineer to lead this journey, tackling complex ML systems challenges in training, serving, and evaluation, and setting architectural standards that impact multiple organizations.
Responsibilities
- Identify and solve complex machine learning systems problems, including model training efficiency, large-scale inference optimization, and ML pipeline reliability.
- Define and own the technical architecture for critical ML infrastructure components, ensuring extensibility, maintainability, and scalability.
- Establish engineering standards and invariants for ML systems to prevent reliability, correctness, and performance issues across teams.
- Drive cross-organizational technical strategy by assessing emerging ML technologies and influencing roadmap decisions.
- Lead the design and delivery of org-wide ML platform initiatives, coordinating across teams to align priorities and drive execution.
- Develop new metrics and data-driven frameworks for evaluating ML system health, model quality, and infrastructure efficiency.
- Mentor and coach engineers on ML system design, debugging, and scalable solutions.
- Collaborate with privacy, security, and compliance teams to design responsible AI systems.
- Identify and eliminate engineering bottlenecks through improved tooling, automation, and process improvements.
- Contribute to recruiting efforts by building relationships with candidates and serving as a technical reference for evaluating talent.
Requirements
- 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 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 stakeholder alignment.
- Experience defining engineering standards, architectural patterns, or platform capabilities adopted broadly across an organization.
- Experience in building large-scale object recognition models.
- Experience collaborating with applied research teams to translate theoretical ML advances into production-grade system improvements.
- Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies.
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact.
- Experience developing novel approaches to ML observability, experiment management, or model lifecycle tooling that improved engineering velocity.
- Experience leading multi-year ML platform or infrastructure migrations in complex, mature technical environments.
- Experience in training large language models to production systems at scale.
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews).
Skills
- Machine Learning Systems
- Model Training Efficiency
- Large-Scale Inference Optimization
- ML Pipeline Reliability
- Technical Architecture
- ML Infrastructure
- Distributed Training Pipelines
- Model Serving Systems
- Feature Stores
- ML Evaluation Frameworks
- Object Recognition Models
- AI Skill Development
- Prompt Engineering
- Context Engineering
- Agent Orchestration
- AI Technologies
- AI Tools Integration
- ML Observability
- Experiment Management
- Model Lifecycle Tooling
- Large Language Models
- Responsible AI Practices
- Risk Assessment
- Bias Mitigation
Location
- Meta
Work Type
- Full-time
Experience Level
- Senior Staff
- 12+ years
About the Company
- Meta is reimagining shopping on Facebook, Instagram, and Muse.
- The vision is to make every piece of media shoppable.
- The Product Recognition and Intelligence stack is the technology 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.