Senior Multimodal AI Engineer - Multimedia Pipelines

Meta; Olympia (WA); On-site; USD 184,000 - 257,000; Full time

Job summary

Meta's Applied AI (AAI) is seeking a senior engineer to own end-to-end multimodal projects across image, video, audio and speech. You will define data needs, build pipelines, and set evaluation standards that scale across teams.

This role emphasizes leadership, mentorship, and collaboration with research, model-training and engineering teams to deliver measurable impact on real-world multimodal AI capabilities.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience.
• 8+ years of programming experience OR 4+ years with a PhD.
• 3+ years building ML systems in production or research settings.
• Strong Python and PyTorch.
• Experience with speech, audio, or music ML and large-scale data pipelines.

Responsibilities

• Set technical direction for a modality or capability area.
• Determine investments and define success metrics with tradeoffs.
• Drive cross-team alignment and build agentic workflows and pipelines.
• Design and own scalable data pipelines and provenance tracking.
• Build evaluation infrastructure with objective metrics and human tests.
• Improve training efficiency and reliability through distributed training and management.
• Reproduce state-of-the-art methods and perform rigorous ablations.
• Mentor engineers, assist hiring and onboarding, elevate evaluation quality.
• Develop generative and representation models for speech, audio, and music.

Skills

Python

PyTorch

ML systems in production

Distributed training

Speech, audio, or music ML

Data pipelines

Education

Bachelor's degree in CS/CE or equivalent

PhD (preferred) or 4+ years exponent

Tools

CUDA

Docker

Job description

Meta's Applied AI (AAI) is seeking a senior engineer to own end-to-end multimodal projects across image, video, audio and speech. You will define data needs, build pipelines, and set evaluation standards that scale across teams.

This role emphasizes leadership, mentorship, and collaboration with research, model-training and engineering teams to deliver measurable impact on real-world multimodal AI capabilities.