Research Scientist Intern, Audio Quality with AI

Meta; Cambridge; On-site; GBP 21,000 - 30,000; Full time

Job summary

Meta Reality Labs Research invites you to join our audio team as an intern focused on speech perception and audio quality. The project targets degradation causes, phonemic error patterns, and the role of multimodal LLMs in evaluating quality.

A twelve to twenty-four week program offers multiple start dates throughout the year. You will work with PhD candidates in Speech and Hearing Science, Auditory Neuroscience, Computational Neuroscience, CS, AI, Generative AI, Transformer models, and ML,

Qualifications

• Candidate currently pursuing a PhD in Speech & Hearing Science, Auditory Neuroscience, Computational Neuroscience, CS, AI, or ML.
• Proficiency with Python, MATLAB, or similar languages.
• Experience with ML platforms such as PyTorch or TensorFlow.
• Background in speech perception, psychoacoustics, or acoustic phonetics.
• Experience deploying novel audio models and LLMs.
• Must have work authorization in the country of employment.
• Experience building audio ML models and LLMs.
• Demonstrated software engineering activity (internships, projects, open source).
• Experience advancing AI methods in CV or audio processing.
• Experience with audio/speech quality assessment and multichannel processing.
• Strong data analysis from large, complex datasets.
• Publication record at top venues is a plus.
• Ability to work cross-functionally and collaborate in teams.
• Intent to return to a degree program after the internship.

Responsibilities

• Investigate why processed speech sounds degraded or robotic.
• Identify systematic phonemic errors affecting perceived quality.
• Explore audio vs video LLM capabilities for speech tasks.
• Collaborate with cross-functional teams on research questions.
• Present findings through reports and demonstrations.

Skills

PhD candidate

Python

ML frameworks

Speech perception

LLMs

Open source

Team collaboration

Communication

Data analysis

Research experience

Education

PhD in field

Tools

PyTorch

TensorFlow

MATLAB

GitHub

Job description

The Meta Reality Labs Research Team brings together a world-class team of researchers, developers, and engineers to create the future of virtual and augmented reality, which together will become as universal and essential as smartphones and personal computers are today. And just as personal computers have done over the past 45 years, AR, VR and MR will ultimately change everything about how we work, play, and connect. We are developing all the technologies needed to enable breakthrough AR glasses and VR headsets, including optics and displays, computer vision, audio, graphics, brain-computer interfaces, haptic interaction, eye/hand/face/body tracking, perception science, and true telepresence. Some of those will advance much faster than others, but they all need to happen to enable AR, VR and MR that are so compelling that they become an integral part of our lives.

In particular, the Meta Reality Labs Research audio team is focused on two goals; creating virtual sounds that are perceptually indistinguishable from reality, and redefining human hearing. See more about our work here: Inside Facebook Reality Labs Research: The future of audio and Filter Out the Noise With Conversation Focus. These two initiatives will allow us to connect people by allowing them to feel together despite being physically apart, and allow them to converse in even the most difficult listening environments.

Meta Reality Labs Research is looking for an intern who is passionate about speech perception and audio quality to investigate why processed speech sometimes sounds degraded or robotic. The project focuses on identifying systematic phonemic errors as causal factors in perceived quality degradation, and linking these errors to human quality and intelligibility judgments. A core method is to explore the capabilities of audio vs video LLMs. This is fundamentally a speech-perception research role; multimodal/LLM methods are a supporting tool rather than the central focus.

Our internships are twelve (12) to twenty four (24) weeks long and we have various start dates throughout the year.

• Currently has, or is in the process of obtaining, a PhD degree in the field of Speech and Hearing Science, Auditory Neuroscience, Computational Neuroscience, Computer Science, Artificial Intelligence, Generative AI, Transformer Models, Machine Learning, Signal Processing or Computer vision
• Experience with Python, Matlab, or similar
• Experience with machine learning software platforms such as PyTorch, TensorFlow, etc
• Background in speech perception, psychoacoustics, or acoustic phonetics
• Experience deploying novel audio computational models and LLMs
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment
• Experience building novel audio computational models and LLMs
• Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. Github)
• Experience in advancing AI techniques, including core contributions to open source libraries and frameworks in computer vision or audio processing
• Experience with audio and speech quality assessment
• Experience with multichannel audio processing
• Experience in visual and acoustic scene analysis
• Experience manipulating and analyzing complex, large scale, high-dimensionality data from varying sources
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or top computer vision and machine learning conferences such as ARO, ASA, NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, ICCV, ECCV, ICASSP, InterSpeech or similar
• Experience in utilizing theoretical and empirical research to solve problems
• Experience working and communicating cross functionally in a team environment
• Intent to return to a degree-program after the completion of the internship/co-op