Senior AI/ML Engineer: Scale ML Pipelines & GenAI

Meta; Los Angeles (CA); On-site; USD 154,000 - 217,000; Full time

Job summary

Meta is seeking an AI/ML Engineer to design, build, and deploy machine learning systems powering widely used products and platforms. The role focuses on developing AI models and pipelines at scale across deep learning, LLMs, recommendation, and generative AI.

You will own end-to-end ML system design, optimize training, and monitor production models while collaborating with product managers and researchers to improve key metrics and reliability.

Qualifications

• 6+ years designing, training and deploying ML/DL models in production.
• Proficiency with PyTorch or TensorFlow and Python.
• Experience with large-scale distributed training and online experimentation (A/B testing).

Responsibilities

• Design, develop, and deploy large-scale ML models and AI pipelines across Meta's product surfaces.
• Own end-to-end technical design of AI/ML systems, evaluate modeling approaches and training strategies.
• Instrument and profile ML training and serving pipelines to reduce latency and improve efficiency.
• Establish and maintain model evaluation frameworks with offline metrics and online experiments.
• Collaborate with PMs, data scientists, and researchers to translate requirements into ML problem form.
• Contribute to AI tooling and infrastructure and promote best practices in deployment.

Skills

Python programming

ML model design

Model deployment

A/B testing

Cross-functional collaboration

Education

Bachelor's degree in Computer Science/Engineering or related field

Tools

PyTorch

TensorFlow

Job description

Meta is seeking an AI/ML Engineer to design, build, and deploy machine learning systems powering widely used products and platforms. The role focuses on developing AI models and pipelines at scale across deep learning, LLMs, recommendation, and generative AI.

You will own end-to-end ML system design, optimize training, and monitor production models while collaborating with product managers and researchers to improve key metrics and reliability.