Software Engineer, Multimedia & Multimodal AI

**Summary:**

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.

**Required Skills:**

Software Engineer, Multimedia & Multimodal AI Responsibilities:

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.

**Minimum Qualifications:**

Minimum Qualifications:

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

**Preferred Qualifications:**

Preferred Qualifications:

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

**Public Compensation:**

$154,003/year to $217,000/year + bonus + equity + benefits

**Industry:** Internet

**Equal Opportunity:**

Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.

Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.