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# Software Engineer, Multimedia & Multimodal AI

- **Company:** The Meta Game, Inc.
- **Location:** Springfield, IL, United States
- **Experience:** Experienced
- **Salary:** $154,003.0 - $217,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Codecs, Distributed Computing Environment, Information Retrieval, Python (Programming Language), Machine Learning, Signal Processing, Management of Software Versions, Pytorch, Prompt Engineering, Generative AI, Agentic-AI, Build Management, Machine Learning Operations, Data Pipelines, Human in the Loop
- **Published:** October 7, 2026
- **Apply:** https://dejobs.org/x/x/BAA4FF89D0F84D028CDDCB5A19A222B0/job/

## About the Role

8. 3+ years building ML systems in production or research settings
 9. strong Python and PyTorch
 10. Demonstrated experience with speech, audio, or music ML - ASR, TTS, audio codecs, music information retrieval, self-supervised audio representation learning, or audio generative modeling
 11. Experience with large-scale data pipelines and distributed training
 12. Track record of translating research ideas into working, measurable systems, 13. Publications at top venues (ICASSP, Interspeech, ISMIR, NeurIPS, ICML, ICLR) in speech, audio, or music
 14. Generative modeling of continuous data (diffusion / flow matching, audio or vision), and demonstrated ability to switch domains and ramp quickly
 15. Audio DSP depth - pitch detection, FFT, real-time signal processing
 16. Experience with disentangled or controllable generation (voice, emotion, style, instrumentation)
 17. Experience building evaluation harnesses and human-eval pipelines for generative audio
 18. Music domain expertise: stem separation, mixing, lyrics/vocal conditioning
 19. Experience designing benchmarks or evaluations for model capability, with attention to grading reliability, reproducibility and label quality
 20. Experience building data pipelines for image, video, audio, speech or complex media formats, including versioning, lineage and provenance
 21. Experience designing AI agents, orchestration, or human-in-the-loop systems
 22. Hands-on experience evaluating or red-teaming multimodal models, or creating the data used to improve them
 23. Understanding of Responsible AI practices and building quality controls into AI output
 24. Experience with zero-to-one work: forming a charter and standing up process while priorities are still moving
 25. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
 26. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
 27. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

## Description

Applied AI (AAI) is Meta's organization focused on making our AI models best-in-class, starting with coding. Within AAI, the Multimedia & MultiModality team covers the multimedia domain across every modality, on both the input and the output side of a model: image, video, audio, speech and music. We work directly with research, model-training and engineering partners across MSL, TBD and FAIR. Current problems include evaluating video experiences, diagnosing multimedia model behavior, producing domain-expert agent tasks, and building the data and measurement pipelines multimodal capabilities are trained and judged against.About the roleYou will take a modality or a capability area, decide what data is worth producing and how it should be measured, and carry it from an open question through to a pipeline that runs and a measurement the org relies on.This is a multimodal role, not a text-only role. You will work across image, video, audio and speech, as model inputs and as model
outputs, and the data and evaluations you own will cover media, not text alone.You will choose where the pod invests, own outcomes beyond your individual contribution, set standards other engineers build against, and raise quality without becoming the review bottleneck., 1. 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.

 2. Design and own data pipelines at scale: ingestion, filtering, pseudo-labeling and captioning with attribute classifiers, and provenance tracking for audio corpora.
 3. 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.
 4. Improve training efficiency and reliability - distributed training, GPU utilization, codec and tokenizer retraining, experiment management.
 5. Reproduce and extend state-of-the-art research: implement new methods from papers into our codebases and run rigorous ablations.
 6. Mentor engineers on the team, contribute to hiring and onboarding, and raise the bar on evaluation and quality practice.
 7. Build and train generative and representation models for speech, sound, and music - including text-, audio-, and video-conditioned generation, infilling, editing, and style transfer.