The posting
Meta is seeking an AI/ML Engineer to design, build, and deploy machine learning systems that power some of the world's most widely used products and platforms. In this role, you will develop and optimize AI models and pipelines at scale, applying techniques across deep learning, large language models, recommendation systems, and generative AI to solve high-impact problems across Meta's product ecosystem. You will work at the intersection of applied research and production engineering, translating cutting-edge AI advances into reliable, scalable systems that serve billions of users.
Responsibilities
- Design, develop, and deploy large-scale machine learning models and AI pipelines across Meta's product surfaces, including ranking, recommendation, and generative AI systems
- Own the end-to-end technical design of AI/ML systems, evaluating architectural trade-offs and selecting appropriate modeling approaches, training strategies, and inference frameworks
- Instrument and profile ML training and serving pipelines to identify performance bottlenecks, reduce latency, and optimize compute efficiency at scale
- Establish and maintain model evaluation frameworks, including offline metrics, online experimentation, and monitoring to ensure production model quality and reliability
- Collaborate with product managers, data scientists, and research scientists to translate product requirements into ML problem formulations and deliver measurable improvements to key metrics
- Contribute to the team's AI tooling and infrastructure by building reusable components, improving developer workflows, and driving adoption of best practices in model development and deployment
- Apply privacy-by-design principles and integrity safeguards throughout the ML development lifecycle, partnering with cross-functional teams to meet data governance and compliance requirements
- Mentor other engineers on ML system design, modeling techniques, and engineering craft, and actively contribute to recruiting and technical community programs
- Use AI-augmented development workflows to expand scope across data analysis, product thinking, and operational readiness beyond core modeling work
- Drive staged rollouts of new models using feature flagging and A/B testing frameworks, proactively monitoring for regressions and leading incident retrospectives to prevent recurrence
Minimum Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- 6+ years of experience designing, training, and deploying machine learning or deep learning models in production environments
- 6+ years of experience with ML frameworks such as PyTorch or TensorFlow, and proficiency in Python for model development and data processing
- Experience with large-scale distributed training, model optimization techniques (e.g., quantization, distillation, pruning), and ML serving infrastructure
- Experience designing and running online experiments (A/B testing) to evaluate model impact on product metrics
- Experience communicating technical decisions and trade-offs in writing to both technical and non-technical stakeholders, including design documents and postmortems
Preferred Qualifications
- Track record of driving cross-team technical initiatives that improved ML platform capabilities or established new modeling standards across an organization
- Experience with large language models, generative AI systems, or foundation model fine-tuning and alignment techniques
- Experience building recommendation or ranking systems at internet scale, including feature engineering, retrieval, and scoring pipelines
- Experience developing ML observability tooling, including dashboards, alerting, and automated anomaly detection for production model health
US: $154,003/year to $217,000/year + bonus + equity
