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 Publications at top venues (ICASSP, Interspeech, ISMIR, NeurIPS, ICML, ICLR) in speech, audio, or music Generative modeling of continuous data (diffusion / flow matching, audio or vision), and demonstrated ability to switch domains and ramp quickly Audio DSP depth - pitch detection, FFT, real-time signal processing Experience with disentangled or controllable generation (voice, emotion, style, instrumentation) Experience building evaluation harnesses and human-eval pipelines for generative audio Music domain expertise: stem separation, mixing, lyrics/vocal conditioning Experience designing benchmarks or evaluations for model capability, with attention to grading reliability, reproducibility and label quality Experience building data pipelines for image, video, audio, speech or complex media formats, including versioning, lineage and provenance Experience designing AI agents, orchestration, or human-in-the-loop systems Hands-on experience evaluating or red-teaming multimodal models, or creating the data used to improve them Understanding of Responsible AI practices and building quality controls into AI output Experience with zero-to-one work: forming a charter and standing up process while priorities are still moving Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologiesMeta builds technologies that help people connect, find communities, and grow businesses. 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.