Senior Multimodal AI Engineer: Pipelines & Evaluation

Meta; Augusta (ME); On-site; USD 184,000 - 257,000; Full time

Job summary

Meta is seeking a senior engineer to lead end-to-end work in Applied AI’s Multimedia & Multimodality team. You’ll own data production, evaluation pipelines, and measurement standards across image, video, audio, and speech modalities, working with research, MSL, TBD, and FAIR partners.

You will drive strategy, mentor engineers, and translate research ideas into scalable, measurable systems that improve multimodal AI capabilities with robust experiments and production-grade pipelines.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• 8+ years of programming experience in a relevant language OR 4+ years experience with a PhD
• 3+ years building ML systems in production or research settings
• strong Python and PyTorch
• Demonstrated experience with speech, audio, or music ML - ASR, TTS, audio codecs, music information retrieval, self-supervised audio representation learning, or audio generative modeling
• Experience with large-scale data pipelines and distributed training
• Track record of translating research ideas into working, measurable systems

Responsibilities

• Set technical direction for a modality or capability area.
• Determining where the team invests, what success means, and the tradeoffs behind both.
• Drive that direction across partner teams, not just inside your own.
• Design and build agentic workflows and pipelines, including human-in-the-loop and expert-in-the-loop designs, to automate data production and scale output past what manual authoring supports.
• Design and own data pipelines at scale: ingestion, filtering, pseudo-labeling and captioning with attribute classifiers, and provenance tracking for audio corpora.
• Build evaluation infrastructure: objective metrics (speaker/style similarity, codec and generator quality), human listening-test pipelines, and the correlation analysis that ties the two together.
• Improve training efficiency and reliability — distributed training, GPU utilization, codec and tokenizer retraining, experiment management.
• Reproduce and extend state-of-the-art research: implement new methods from papers into our codebases and run rigorous ablations.
• Mentor engineers on the team, contribute to hiring and onboarding, and raise the bar on evaluation and quality practice.
• Build and train generative and representation models for speech, sound, and music — including text-, audio-, and video-conditioned generation, infilling, editing, and style transfer.

Skills

Python

Multimodal ML

Distributed training

Data pipelines

Leadership

Mentoring

System design

Experimentation

Education

Bachelor's degree in CS/Engineering or equivalent

PhD with 4+ years experience

Tools

PyTorch

Python

Job description

Meta is seeking a senior engineer to lead end-to-end work in Applied AI’s Multimedia & Multimodality team. You’ll own data production, evaluation pipelines, and measurement standards across image, video, audio, and speech modalities, working with research, MSL, TBD, and FAIR partners.

You will drive strategy, mentor engineers, and translate research ideas into scalable, measurable systems that improve multimodal AI capabilities with robust experiments and production-grade pipelines.