Meta is seeking a Machine Learning Engineer to design, build, and deploy machine learning systems across a broad range of product areas including ranking, recommendations, natural language understanding, computer vision, and integrity. In this role, you will own the full ML development lifecycle — from data pipeline construction and feature engineering through model training, evaluation, and production deployment — contributing directly to systems that serve billions of users at global scale.
Responsibilities
- Design and implement end-to-end machine learning pipelines spanning data ingestion, feature engineering, model training, offline evaluation, and production serving
- Develop and optimize models for large-scale applications such as ranking, recommendations, classification, and natural language understanding
- Instrument model performance with robust monitoring, alerting, and dashboards to detect regressions and maintain production reliability
- Drive A/B experiment design and analysis to validate model improvements, articulate trade-offs, and make data-informed launch decisions
- Architect scalable ML infrastructure and reusable components that accelerate development velocity across the team
- Identify and resolve complex model quality issues through systematic debugging, root cause analysis, and defensive engineering practices
- Collaborate with product managers, data scientists, and software engineers to translate product requirements into ML problem formulations and technical roadmaps
- Define and evolve ML platform standards, coding best practices, and testing frameworks to improve engineering quality across the organization
- Mentor other engineers on ML methodology, system design, and engineering craft, and contribute to recruiting by conducting technical interviews
Qualifications
- 6+ years of experience developing and deploying machine learning models in production environments
- Experience with large-scale ML frameworks such as PyTorch, TensorFlow, or JAX, and proficiency in Python for model development and data processing
- Experience designing and optimizing ML systems for ranking, recommendations, classification, or natural language understanding at scale
- Experience with feature engineering, offline and online model evaluation, and A/B experimentation methodologies
- Experience building reliable, monitored ML pipelines with observability tooling including dashboards, alerting, and logging Familiarity with ML platform infrastructure including feature stores, model registries, and automated retraining pipelines
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Experience with distributed training frameworks and large-scale model optimization techniques such as quantization, distillation, or pruning
- Track record of driving cross-functional ML projects from problem definition through production impact, including influencing technical direction across teams
- 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 (e.g., efficiency gains, quality improvements)
- Experience applying ML to integrity, personalization, or real-time inference systems with strict latency requirements