Job Description

Meta was built to help people connect and share, and our technologies play a critical role in how people around the world communicate, create, and experience media. A research internship at Meta offers the opportunity to advance the state of the art while working on systems used across global products.

Meta is seeking Research Scientist Interns to join the Media Foundation Core Audio team. We develop audio technologies used across WhatsApp, Messenger, Instagram, Facebook, and Meta AI experiences, spanning real-time calling, conversational AI, voice and media messaging, Live, and video-on-demand.

Interns will work on one focused research problem at the intersection of machine learning, audio and speech signal processing, human perception, and efficient systems. Potential areas include streaming speech and audio enhancement for calling and conversational AI; restoration and super-resolution for creator media and video; neural audio codecs and compression for speech, music, and general audio; perceptual and context-aware quality metrics; multimodal evaluation; and efficient on-device or server inference through model architecture, distillation, quantization, and runtime optimization. Projects will be selected based on the intern’s expertise and current team priorities.

The work combines research depth with real-world constraints such as latency, bitrate, compute, memory, power, robustness, and safe fallback. Interns will collaborate with research scientists, software engineers, and product partners to define the problem, build and evaluate solutions, and identify a credible path from research result to product impact.

Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long, with various start dates throughout the year.

Responsibilities

Perform research to advance audio, speech, multimodal, and machine learning technologies.

• Design, train, and evaluate models for one or more audio applications, such as enhancement, restoration, compression, generation, or quality understanding.
• Build reproducible evaluation pipelines using objective metrics, human listening tests, robustness analysis, and system-level measurements.
• Analyze and improve model quality, efficiency, latency, scalability, and stability under practical device, network, and serving constraints.
• Collaborate with researchers, software engineers, and cross-functional partners; communicate research plans, progress, tradeoffs, and results.
• Publish research results and contribute to research that can be applied to Meta product development.

Qualifications

Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Artificial Intelligence, Natural Language Processing, Speech Recognition, Sentiment Analysis, Computer Vision, or relevant technical field

• Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment
• Experience with Python, C++, C, Java or other related language
• Experience with deep learning frameworks such as Pytorch or Tensorflow
• Familiarity with machine learning and common deep learning architectures Intent to return to the degree program after the completion of the internship/co-op
• Proven track record of solid research achievements as demonstrated by grants, fellowships, patents, as well as publications at leading AI conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP and KDD
• Prior research or project experience in one or more of the following areas: reinforcement learning, pre-training and supervised fine-tuning of large language models, sequence modeling, diffusion models, and their applications in recommendation systems
• Demonstrated software development experience via tech internships, work experience, coding competitions, or widely used contributions in open source machine-learning repositories
• Experience working and communicating cross functionally in a fast-paced team environment. Ideal candidates should have the ability to quickly understand and identify the research opportunities behind real-world applications, select the appropriate ML methods to explore, and proactively drive the iterations based on clear analysis of the current result